pragmatic big data view for translational...
TRANSCRIPT
Translational Medicine
Gary Berg-Cross
SOCoP EarthCube SO group
RDA WG on Data Foundations amp Terminology
Scimaps Advisory Board
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Broad Biology Landscape
Medical Genomics Large-scale Genomics
Network
Biology Opportunities to explore data
and accelerate process of
discovery amp treatment
New fields of related research amp application
Lab Medical Practice (15Y)
Bench
Bedside Med-Concepts
SNOMED-CT
Patient
Encounters
Observations
Trials
Outline of Remaining Discussion
A few slides on general ontology issues
Import some strategies from the EarthCube Semantics and
Ontology WG effort
Slides show some adaptation from earth science to medical
domains
Keeping an eye on start up work on data sharing from Research
Data Alliance
Sound Ontology Development should
Leverage some Existing Semantic Theories
Theory of Parts Mereology or mereotopology Is parthood transitive
Some are some not
Theory of Wholes what is the difference between a part and a whole
The whole of a treatment
Theory of Essence and Identity what are essential ie necessary properties
If you lose a necessary property you lose identity If patient Jim loses an arm he‟s still Jim but not if he dies in treatment
When is a treatment a different treatment a genetic variant
Theory of Dependence some things and properties depend on others
Theory of Qualities features attributes qualia quality spaces
Theory of Composition and Constitution What makes up Diabetes or a treatment
bull Adapted from Guarino N Multiple tutorials 2002-2010 But also seen
in BioPortal work
There are Additional Theories to
Consider for Ontology Development
ndashTheory of Participation amp Roles (very important for Health Delivery part) a conceptual framework for describing and analyzing communicative phenomena agency community problem-solving intersects formal pragmatics speech acts intents etc
ndashTheory of Representation how does one thing represent another Models represent transcription influence of cell function Treatment plan or specification represent real delivery acts
ndashTheory of Time Spacetime and Events Cellular Events and Organ States how to bridge these
Adapted from Guarino N Multiple tutorials 2002-2010
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Bridging Vocabularies amp Ontologies
Lab Medical Practice
ldquoEHR-driven genomic researchrdquo
(EDGR) EHR data linked to DNA
samples
Trials
Technology Insertion
Lightweight Methods
TM use cases ndash eg Alzheimer along the TM spectrum
Differing granular units emerge from interactions
Ontologies
TMO etc
Push
Semantics and Ontologies For Translational Medicine 7
Relevant Ideas from EarthCube Semantics amp
Ontology and RDA
For Example
Knowledge-based infrastructures using semantic annotation of metadata realized using shared vocabularies typically ontologies
Semantic technologies enable better searching metadata catalogs
Many Semantic Technology parts but
an important driver has been the Semantic
Web amp Linked Data framework LOD delivering a platform agnostic variant of ODBC and
JDBC Data Source Names (DSNs) via hyperlinks
Ontologies amp KR languages that restrict the interpretation of domain vocabulary towards their intended meaning
Enable reasoning services
Semantic
Approaches
Managing Scientific Data From Data Integration to Scientific Workflows
httpuserssdscedu~ludaeschPapergsa-smspdf (Ludascher et al)
Semantic Automated Discovery
and Integration
httpsadiframeworkorg ldquoShow me patients whose
creatinine level is increasing
over time along with their
latest BUN and creatinine
levelsrdquo
Linking Open
Biomedical Data
(twc-lobd) httpcodegooglec
omptwc-lobd
TMO (Dumontier et
al 2010)
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Broad Biology Landscape
Medical Genomics Large-scale Genomics
Network
Biology Opportunities to explore data
and accelerate process of
discovery amp treatment
New fields of related research amp application
Lab Medical Practice (15Y)
Bench
Bedside Med-Concepts
SNOMED-CT
Patient
Encounters
Observations
Trials
Outline of Remaining Discussion
A few slides on general ontology issues
Import some strategies from the EarthCube Semantics and
Ontology WG effort
Slides show some adaptation from earth science to medical
domains
Keeping an eye on start up work on data sharing from Research
Data Alliance
Sound Ontology Development should
Leverage some Existing Semantic Theories
Theory of Parts Mereology or mereotopology Is parthood transitive
Some are some not
Theory of Wholes what is the difference between a part and a whole
The whole of a treatment
Theory of Essence and Identity what are essential ie necessary properties
If you lose a necessary property you lose identity If patient Jim loses an arm he‟s still Jim but not if he dies in treatment
When is a treatment a different treatment a genetic variant
Theory of Dependence some things and properties depend on others
Theory of Qualities features attributes qualia quality spaces
Theory of Composition and Constitution What makes up Diabetes or a treatment
bull Adapted from Guarino N Multiple tutorials 2002-2010 But also seen
in BioPortal work
There are Additional Theories to
Consider for Ontology Development
ndashTheory of Participation amp Roles (very important for Health Delivery part) a conceptual framework for describing and analyzing communicative phenomena agency community problem-solving intersects formal pragmatics speech acts intents etc
ndashTheory of Representation how does one thing represent another Models represent transcription influence of cell function Treatment plan or specification represent real delivery acts
ndashTheory of Time Spacetime and Events Cellular Events and Organ States how to bridge these
Adapted from Guarino N Multiple tutorials 2002-2010
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Bridging Vocabularies amp Ontologies
Lab Medical Practice
ldquoEHR-driven genomic researchrdquo
(EDGR) EHR data linked to DNA
samples
Trials
Technology Insertion
Lightweight Methods
TM use cases ndash eg Alzheimer along the TM spectrum
Differing granular units emerge from interactions
Ontologies
TMO etc
Push
Semantics and Ontologies For Translational Medicine 7
Relevant Ideas from EarthCube Semantics amp
Ontology and RDA
For Example
Knowledge-based infrastructures using semantic annotation of metadata realized using shared vocabularies typically ontologies
Semantic technologies enable better searching metadata catalogs
Many Semantic Technology parts but
an important driver has been the Semantic
Web amp Linked Data framework LOD delivering a platform agnostic variant of ODBC and
JDBC Data Source Names (DSNs) via hyperlinks
Ontologies amp KR languages that restrict the interpretation of domain vocabulary towards their intended meaning
Enable reasoning services
Semantic
Approaches
Managing Scientific Data From Data Integration to Scientific Workflows
httpuserssdscedu~ludaeschPapergsa-smspdf (Ludascher et al)
Semantic Automated Discovery
and Integration
httpsadiframeworkorg ldquoShow me patients whose
creatinine level is increasing
over time along with their
latest BUN and creatinine
levelsrdquo
Linking Open
Biomedical Data
(twc-lobd) httpcodegooglec
omptwc-lobd
TMO (Dumontier et
al 2010)
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Outline of Remaining Discussion
A few slides on general ontology issues
Import some strategies from the EarthCube Semantics and
Ontology WG effort
Slides show some adaptation from earth science to medical
domains
Keeping an eye on start up work on data sharing from Research
Data Alliance
Sound Ontology Development should
Leverage some Existing Semantic Theories
Theory of Parts Mereology or mereotopology Is parthood transitive
Some are some not
Theory of Wholes what is the difference between a part and a whole
The whole of a treatment
Theory of Essence and Identity what are essential ie necessary properties
If you lose a necessary property you lose identity If patient Jim loses an arm he‟s still Jim but not if he dies in treatment
When is a treatment a different treatment a genetic variant
Theory of Dependence some things and properties depend on others
Theory of Qualities features attributes qualia quality spaces
Theory of Composition and Constitution What makes up Diabetes or a treatment
bull Adapted from Guarino N Multiple tutorials 2002-2010 But also seen
in BioPortal work
There are Additional Theories to
Consider for Ontology Development
ndashTheory of Participation amp Roles (very important for Health Delivery part) a conceptual framework for describing and analyzing communicative phenomena agency community problem-solving intersects formal pragmatics speech acts intents etc
ndashTheory of Representation how does one thing represent another Models represent transcription influence of cell function Treatment plan or specification represent real delivery acts
ndashTheory of Time Spacetime and Events Cellular Events and Organ States how to bridge these
Adapted from Guarino N Multiple tutorials 2002-2010
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Bridging Vocabularies amp Ontologies
Lab Medical Practice
ldquoEHR-driven genomic researchrdquo
(EDGR) EHR data linked to DNA
samples
Trials
Technology Insertion
Lightweight Methods
TM use cases ndash eg Alzheimer along the TM spectrum
Differing granular units emerge from interactions
Ontologies
TMO etc
Push
Semantics and Ontologies For Translational Medicine 7
Relevant Ideas from EarthCube Semantics amp
Ontology and RDA
For Example
Knowledge-based infrastructures using semantic annotation of metadata realized using shared vocabularies typically ontologies
Semantic technologies enable better searching metadata catalogs
Many Semantic Technology parts but
an important driver has been the Semantic
Web amp Linked Data framework LOD delivering a platform agnostic variant of ODBC and
JDBC Data Source Names (DSNs) via hyperlinks
Ontologies amp KR languages that restrict the interpretation of domain vocabulary towards their intended meaning
Enable reasoning services
Semantic
Approaches
Managing Scientific Data From Data Integration to Scientific Workflows
httpuserssdscedu~ludaeschPapergsa-smspdf (Ludascher et al)
Semantic Automated Discovery
and Integration
httpsadiframeworkorg ldquoShow me patients whose
creatinine level is increasing
over time along with their
latest BUN and creatinine
levelsrdquo
Linking Open
Biomedical Data
(twc-lobd) httpcodegooglec
omptwc-lobd
TMO (Dumontier et
al 2010)
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Sound Ontology Development should
Leverage some Existing Semantic Theories
Theory of Parts Mereology or mereotopology Is parthood transitive
Some are some not
Theory of Wholes what is the difference between a part and a whole
The whole of a treatment
Theory of Essence and Identity what are essential ie necessary properties
If you lose a necessary property you lose identity If patient Jim loses an arm he‟s still Jim but not if he dies in treatment
When is a treatment a different treatment a genetic variant
Theory of Dependence some things and properties depend on others
Theory of Qualities features attributes qualia quality spaces
Theory of Composition and Constitution What makes up Diabetes or a treatment
bull Adapted from Guarino N Multiple tutorials 2002-2010 But also seen
in BioPortal work
There are Additional Theories to
Consider for Ontology Development
ndashTheory of Participation amp Roles (very important for Health Delivery part) a conceptual framework for describing and analyzing communicative phenomena agency community problem-solving intersects formal pragmatics speech acts intents etc
ndashTheory of Representation how does one thing represent another Models represent transcription influence of cell function Treatment plan or specification represent real delivery acts
ndashTheory of Time Spacetime and Events Cellular Events and Organ States how to bridge these
Adapted from Guarino N Multiple tutorials 2002-2010
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Bridging Vocabularies amp Ontologies
Lab Medical Practice
ldquoEHR-driven genomic researchrdquo
(EDGR) EHR data linked to DNA
samples
Trials
Technology Insertion
Lightweight Methods
TM use cases ndash eg Alzheimer along the TM spectrum
Differing granular units emerge from interactions
Ontologies
TMO etc
Push
Semantics and Ontologies For Translational Medicine 7
Relevant Ideas from EarthCube Semantics amp
Ontology and RDA
For Example
Knowledge-based infrastructures using semantic annotation of metadata realized using shared vocabularies typically ontologies
Semantic technologies enable better searching metadata catalogs
Many Semantic Technology parts but
an important driver has been the Semantic
Web amp Linked Data framework LOD delivering a platform agnostic variant of ODBC and
JDBC Data Source Names (DSNs) via hyperlinks
Ontologies amp KR languages that restrict the interpretation of domain vocabulary towards their intended meaning
Enable reasoning services
Semantic
Approaches
Managing Scientific Data From Data Integration to Scientific Workflows
httpuserssdscedu~ludaeschPapergsa-smspdf (Ludascher et al)
Semantic Automated Discovery
and Integration
httpsadiframeworkorg ldquoShow me patients whose
creatinine level is increasing
over time along with their
latest BUN and creatinine
levelsrdquo
Linking Open
Biomedical Data
(twc-lobd) httpcodegooglec
omptwc-lobd
TMO (Dumontier et
al 2010)
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
There are Additional Theories to
Consider for Ontology Development
ndashTheory of Participation amp Roles (very important for Health Delivery part) a conceptual framework for describing and analyzing communicative phenomena agency community problem-solving intersects formal pragmatics speech acts intents etc
ndashTheory of Representation how does one thing represent another Models represent transcription influence of cell function Treatment plan or specification represent real delivery acts
ndashTheory of Time Spacetime and Events Cellular Events and Organ States how to bridge these
Adapted from Guarino N Multiple tutorials 2002-2010
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Bridging Vocabularies amp Ontologies
Lab Medical Practice
ldquoEHR-driven genomic researchrdquo
(EDGR) EHR data linked to DNA
samples
Trials
Technology Insertion
Lightweight Methods
TM use cases ndash eg Alzheimer along the TM spectrum
Differing granular units emerge from interactions
Ontologies
TMO etc
Push
Semantics and Ontologies For Translational Medicine 7
Relevant Ideas from EarthCube Semantics amp
Ontology and RDA
For Example
Knowledge-based infrastructures using semantic annotation of metadata realized using shared vocabularies typically ontologies
Semantic technologies enable better searching metadata catalogs
Many Semantic Technology parts but
an important driver has been the Semantic
Web amp Linked Data framework LOD delivering a platform agnostic variant of ODBC and
JDBC Data Source Names (DSNs) via hyperlinks
Ontologies amp KR languages that restrict the interpretation of domain vocabulary towards their intended meaning
Enable reasoning services
Semantic
Approaches
Managing Scientific Data From Data Integration to Scientific Workflows
httpuserssdscedu~ludaeschPapergsa-smspdf (Ludascher et al)
Semantic Automated Discovery
and Integration
httpsadiframeworkorg ldquoShow me patients whose
creatinine level is increasing
over time along with their
latest BUN and creatinine
levelsrdquo
Linking Open
Biomedical Data
(twc-lobd) httpcodegooglec
omptwc-lobd
TMO (Dumontier et
al 2010)
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Opportunities in Translational Research Cycle Many
Biological amp BedsideMedical (BampB) Areas of Big Data
Bridging Vocabularies amp Ontologies
Lab Medical Practice
ldquoEHR-driven genomic researchrdquo
(EDGR) EHR data linked to DNA
samples
Trials
Technology Insertion
Lightweight Methods
TM use cases ndash eg Alzheimer along the TM spectrum
Differing granular units emerge from interactions
Ontologies
TMO etc
Push
Semantics and Ontologies For Translational Medicine 7
Relevant Ideas from EarthCube Semantics amp
Ontology and RDA
For Example
Knowledge-based infrastructures using semantic annotation of metadata realized using shared vocabularies typically ontologies
Semantic technologies enable better searching metadata catalogs
Many Semantic Technology parts but
an important driver has been the Semantic
Web amp Linked Data framework LOD delivering a platform agnostic variant of ODBC and
JDBC Data Source Names (DSNs) via hyperlinks
Ontologies amp KR languages that restrict the interpretation of domain vocabulary towards their intended meaning
Enable reasoning services
Semantic
Approaches
Managing Scientific Data From Data Integration to Scientific Workflows
httpuserssdscedu~ludaeschPapergsa-smspdf (Ludascher et al)
Semantic Automated Discovery
and Integration
httpsadiframeworkorg ldquoShow me patients whose
creatinine level is increasing
over time along with their
latest BUN and creatinine
levelsrdquo
Linking Open
Biomedical Data
(twc-lobd) httpcodegooglec
omptwc-lobd
TMO (Dumontier et
al 2010)
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine 7
Relevant Ideas from EarthCube Semantics amp
Ontology and RDA
For Example
Knowledge-based infrastructures using semantic annotation of metadata realized using shared vocabularies typically ontologies
Semantic technologies enable better searching metadata catalogs
Many Semantic Technology parts but
an important driver has been the Semantic
Web amp Linked Data framework LOD delivering a platform agnostic variant of ODBC and
JDBC Data Source Names (DSNs) via hyperlinks
Ontologies amp KR languages that restrict the interpretation of domain vocabulary towards their intended meaning
Enable reasoning services
Semantic
Approaches
Managing Scientific Data From Data Integration to Scientific Workflows
httpuserssdscedu~ludaeschPapergsa-smspdf (Ludascher et al)
Semantic Automated Discovery
and Integration
httpsadiframeworkorg ldquoShow me patients whose
creatinine level is increasing
over time along with their
latest BUN and creatinine
levelsrdquo
Linking Open
Biomedical Data
(twc-lobd) httpcodegooglec
omptwc-lobd
TMO (Dumontier et
al 2010)
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine 8
Graphic Overview of Strategy Semantics amp Ontologies
-Ideas from EarthCube SO group
Knowledge Infrastructure Vision Community Understanding of
Semantic role and value
Guiding principles ndashShare Methods
1 Understand the Drivers (LoD)
2 Lightweight -opportunistic
1 Modular patterns
2 Resuablehellip
3 Semantic interoperability with
semantic heterogeneity
4 Bottom-up amp top-down approaches
5 Domain - ontology engineer teams
6 Formalized bodies of knowledge
across TM domains
7 Reasoning services
ldquonew tech
Insertionrdquo
TM
Genomics Proteomics Disease
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine 9
Linked B amp B Data Applications Horizontal amp Vertical Integration
chemical
DBs
Genomic
Domain
DBs
Proteomic
Domain
DBs
Disease
Domain
DBs
Treatment
Domain
DBs
Treatment
things
Chem
things
Proteomic
things Genomic
things
Disease
things
Sigma
Etc
Marbles
Etc
Dbpedia
Mobile
Etc
RDFs
OWL
After Christian Bizer
The Web of Linked
Data (26072009)
URIBurner
Etc
EHR
BioPaths
Domain
DBs
Observations
Longitudinal
data
Lab Testhellip
TMO ~ 75 classes for material entities (molecule protein cell lines
pharm preps)roles (subject target active ingredient) processes
(diagnosis study intervention) amp info entities(eg dosage
mechanism of action signsymptom
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Design Pattern Annotation Approach Premise improve discovery re-usage and integration of TM data from
different sources by means of semantic annotation
Process
Leverage ontology design pattern flexible and self-contained building
blocks more suitable for simple annotation of interdisciplinary multi-
thematic and multi-perspective data than foundational ontologies alone
Annotate based on the common vocabularies with domain-specific
aspects added on top of them Example of Semantic Trajectory
Axiom - an x is enforced to have a timestamp and a
position associated An x must belong to a trajectory
enforced by this Axiom
Fix le Exists atTimeOWL-TimeTemporal Thing and le
hasLocationPosition and hasFix_ SemanticTrajectory
helliphellipWe automatize the creation of proper-
ties hasNext hasSuccessor hasPrevious and
hasPredecessor making use of DL Axioms etchellip
Schematic Basis
for annotation
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine
11
Use Case and Competency Question Methods
Guide lightweight design development from use cases and
Competency Questions needed for applications (Tells us what an Ontology is good for)
Semantic Trajectory Qs
Show moving objects which stop at x and y(could be only x and y)
Show the objects which move at a ground speed of 04 ms
Each moving object(x) has attributes (temperature ground speed heading direction) which describe s status and the environment at that x
Show the trajectories which cross national parks
Parks as Points of Interest and available on maps by lat-lon
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine
12
Lightweight Building Block Illustration
Low hanging fruit leverages initial vocabularies amp existing
conceptual models to ensure that semantics models are available
for use in early stages of work
Reduced entry barrier for domain scientists to contribute dataLoD
directly applicable to a variety of trajectory datasets and
easily extensible eg to align with existing ontologies foundational
ontologies or other domain specific vocabularies
Simple partspatterns amp direct relations to data Triple like parts
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For EarthCube 13
Incremental Approaches Richer Schemata
Simple Feature-State Model (from GRAIL) becomes a richer schema
Warm patienthellip
Too simple a triple
Richer schema
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For EarthCube 14
Allow for Bottom-up amp Top-down Approaches to Semantics
This will ensure a vertical integration from the observations-based data level up
to the theory-driven formalization of key domain facts (such networks in System
Biology)
Transcription
Process
Integrating systems biology models amp biomedical ontologies RHoehndorf
MDumontier JGennari S Wimalaratne5 Bde Bono D Cook and Geo G Koutos
Lab amp Clinical
Observations
From Systems
Biology Markup
Language
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
After The Translational Medicine Ontology KB driving personalized
medicine by bridging the gap between bench and bedside Joanne S
Luciano et al J Biomed Semantics 2011 2(Suppl 2) S1
15
Do it ldquoBehind the Scenesrdquo
Automate linking the
BampB data via terms and
Correlated measurements
Patient Observations
Lab Tests
Formalize
BampB Models amp data
relations
Semantic technologies require knowledge of formal logic that is
unfamiliar to most BioMedical scientists So Institutionalize what we can
ldquoYou mean I donrsquot have to be able to read
XML RDF or OWL Yea
EPR
Reengineered
EHR biochemical
haematological
amp SNP profile
Acceptable
diagnosis of AD
with behavioral
assessments
cognitive tests
and appropriate
brain scan
BioInformation
Models
Tagging
Annotation etc
Treatment
Plan
SNP verdict
efficacious
disease receptor positive
ndash toxic metabolites
Drug available
Pharmacogenomics
DB
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
16
Communicating an Understandable Value Proposition
What Uncover hidden heterogeneities amp make them explicit
This affords incompatibility discovery prevent users from mixing apples and oranges
How Promote common vocabularies for annotating and describing data using terms formalized ontologies
Leverage vast number of available repositories ontologies methods standards and tools that support scientists in publishing sharing and discovering data
Value gt expected from annotation using simple metadata
But the community needs to understand the semantic technologies
vision-infrastructure-value in a non-technical language
After
Patrick Maueacute Roth Marcell
(2012) Lost in Translation ndash
Mediating between
distributed environmental
resources 6th
International Congress on
Environmental Modelling
and Software (iEMSs) 2012
Leipzig
Translational Medicine
Objectsconcepts
TM Ontology
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For EarthCube
17
Next Generation TM Vision
Support distributed and interdisciplinary knowledge infrastructures to handle exchange
integration and reuse of heterogeneous Big Data
Linked Data Argument ndash
Linked Data is an easily adoptable
and ready-to-use paradigm that
enables data integration and
interoperation by opening up data
silos
- Part of a knowledge infrastructure
Digital Medicine
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For EarthCube 18
Seven (or so) Guiding Principles for
Facilitating Implementation and Application
Methods
1 Driven by concrete TM use cases needs
2 Use lightweight (semantic) approaches
3 Foster semantic interoperability without restricting extant semantic heterogeneity
4 Employ bottom-up and top-down semantics approaches
5 Involve amp enable domain experts assisted by ontology engineers
6 Continue work with S amp O to build a formal body of knowledge in the various health domains involved in TM
Technology
7 Employ classical and non-classical reasoning services
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For EarthCube 19
Understand Requirements
Concrete Use Cases
Work should be driven by use cases generated by members of the TM community ndash eg Alzheimer
Collecting a set of use cases from groups along the TM spectrum
Need a substantial study of interconnected use cases which expose requirements related to data models and tools
which have clear implications for data interoperability ontology and semantics infrastructure
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For EarthCube 20
Foster Semantic Interoperability without restricting
underlying Semantic Heterogeneity
Problem Heterogeneity is introduced by the diverse clinical amp research communities
Solution Provide methods that enable users to flexibly load and combine different ontologies instead of hardwiring data to particular ontologies and thus hinder their flexible reusability
Example - Work from modular building blocks with microtheories of locally valid semantics
Manage multiple small internally consistent ontologies and focus on interrelations as needed for inter-operation
S Duce amp K Janowicz
ldquoMicrotheories for SDIrdquo
2010
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine 21
Integrated KE Teams amp Process- domain
experts and semantic technologists
Projects must be structured so domain experts are active participants in building semantic models from use cases thru conceptualization to validating final products
Use
Consistent strategies amp methods
Facilitate good documentation and
May need regular Educational Workshops on how to do this and also publish retrieve and integrate data models and workflows
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Practical model for the design amp execution of
translational informatics projects
From Philip Payne‟s Biomedical Knowledge Integration Dec 2012
Illustrates major phases
exemplary input or output
resources and data sets
Insert Semantics
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine
23
Methods for Useful Formalized Bodies of
Knowledge
Apply ontological engineeringKE to capture the body of knowledge for various TM domains
Conceptualization of local models
Work on primitives ie base symbols for such ontologies
Ground primitives in real observations and align them to knowledge patterns
Track categorical data back to measurements using provenance
(eg RDF in context)
Work to make ontologies first class citizens usable by statistic methods
After construction phase organize building blocks amp ontological models
To help access data domain models and their use in tools
This can also be used for educational applications for learning about domain concepts and extracting information
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies Translational Medicine 24
Provide Reasoning Services for Products
Developed by our Methods Behind the scenes - classical and non-classical reasoning services leveraging resources for
organizing and accessing data
models and tools
learning about them and
extracting information
Reasoning services can be used to
Develop friendly user interfaces
Dialog systems
Scientist assistingassociate services (chains) for
discovering data
integrity constraint checking
generation of new knowledge and hypothesis testing
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Research Data Alliance Vision Purpose Partners
Vision Researchers around the world share amp use research data without
barriers
Purpose accelerate international data-driven innovation and discovery by
facilitating research data sharing and exchange
use and re-use
standards harmonization and
discoverability
This will be achieved through the development and adoption of infrastructure policy practice standards and other deliverables
Partners brought into existence by an initial 3 research funding organizations
The Australian Commonwealth Government through the Australian National Data Service supported by the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative
The European Commission through the iCordi project funded under the 7th Framework Program
In US through the RDAUS activity funded by the National Science Foundation
httprd-allianceorgorgindexhtml
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
RDA Cites Big Cites in HC as Opportunity
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Why RDA For Coordinated Action
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Bob Chen RDA is intersection of internet culture and science culture
EcosystemsSocio-Techical network view
need diversity and many types of it and often rely on bdquoweak‟ interactions
SO hellip partnerships coordination networks work dependencies etc
Super Nodes ndash people CI roles Roles responsibilities and resulting delegation to the smaller nodes around the super nodes (Peter Fox)
RDA networking global data initiatives in era of
Open Science The European
Commission the US
and Australia have
formally launched a
collaboration called
the Research Data
Alliance in
Gothenburg Sweden
People
amp Orgs
CIamp Data
Roles
Sociological
Technical
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
We need more than Ontologies - Joint Aspects of
Socio-technical system(s)
Innovation is driven by several things
1 Changing relationships between scientists institutions
organisms methods and technologies
2 Changing topology of research literature dominant
topics questions problem areas research fronts
3 Changing relationships between concepts Manfred D Laubichler
Sociological
ndash people and
groups of
people
Technical
ndash
Semantics Semantic
community
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand
Semantics and Ontologies For Translational Medicine 30
Closing Remarks and Comments
While many details need to be added these should come from continued dialog such as afforded by VoCamps amp TM domain conferences
Goals going forward
Converge on and integrate more (BIG) TM data in an open transparent and inclusive manner
Make adoption easier for clinicians researchers (educators)
Expose data and information to knowledge creation through data-enabled science
Enhance Interworkability of data and information
All of these of course depends on our tools and techniques scaling up to the magnitude of the problems at hand