ontology and the future of biomedical research barry smith

Ontology and the Future of Biomedical Research

Barry Smithhttp://ifomis.org

Institute for Formal Ontology and Medical Information Science

Saarland University

From chromosome

to disease

Problem:how to reason with data deriving from different sources, each of which uses its own system of classification ?

Solution:

Ontology !

Examples of current needs for ontologies in biomedicine

– to enforce semantic consistency within a database

– to enable data sharing and re-use– to enable data integration (bridging

across data at multiple granularities)

– to allow querying

What is needed

strong general purpose classification hierarchies created by domain specialists clear, rigorous definitionsthoroughly tested in real use casesupdated in light of scientific advance

The actuality (too often)

myriad special purpose ‘light’ ontologies, prepared by ontology engineers and deposited in internet ‘repositories’ or ‘registries’

ontologies for ‘agent’

General trend

on the part of NIH, FDA and other bodies to consolidate ontology-based standards for the communication and processing of biomedical data.

Responses to this trend

Old: UMLS (Unified Medical Language System) – rooted in the faithfulness to the ways language is used by different medical communities

SNOMED

DEMONS

U M L S

– congenital absent nipple is_a nipple– cancer documentation is_a cancer– disease prevention is_a disease– repair and maintenance of wheelchair is_a

disease– water is_a nursing phenomenon– part-whole =def. a nursing phenomenon with

topology part-whole

U M L S

MeSH

MeSH Descriptors Index Medicus Descriptor Anthropology, Education, Sociology and Social Phenomena (MeSH Category) Social Sciences Political Systems National Socialism

MeSH

National Socialism is_a Political SystemsNational Socialism is_a Anthropology ...National Socialism is_a Social SciencesNational Socialism is_a MeSH Descriptors

New: Semantic Web deposits

Pet Profile Ontology

Review Vocabulary

Band Description Vocabulary

Musical Baton Vocabulary

MusicBrainz Metadata Vocabulary

Kissology

http://www.w3.org/

Beer Ontology

all instances of hops that have ever existed are necessarily ingredients of beer.

some nice computational resources, but low expressivityand few genuinely scientific demonstration cases

OWL-based ontologies …

OWL’s syntactic regimentation is not enough to ensure high-quality

ontologies

– the use of a common syntax and logical machinery and the careful separating out of ontologies into namespaces does not solve the problem of ontology integration

Both UMLS- and OWL-type responses involve ad hoc creation of new terminologies by each community

Many of these terminologies remain as torsos, gather dust, poison the wells, ...

How to do better?How to create the conditions for a step-by-step evolution towards high quality ontologies in the biomedical domainwhich will serve as stable attractors for clinical and biomedical researchers in the future?

A basic distinction

type vs. instance

science text vs. clinical document

dog vs. Fido

Instances are not represented in an ontology built for

scientific purposesIt is the generalizations that are

important

(but instances must still be taken into account)

A 515287 DC3300 Dust Collector Fan

B 521683 Gilmer Belt

C 521682 Motor Drive Belt

Catalog vs. inventory

Ontology Types Instances

Ontology = A Representation of Types

Ontology = A Representation of Types

Each node of an ontology consists of:

• preferred term (aka term)

• term identifier (TUI, aka CUI)

• synonyms

• definition, glosses, comments

Each term in an ontology represents exactly one type

hence ontology terms should be singular nouns

National Socialism is_a Political Systems

An ontology is a representation of types

We learn about types in reality from looking at the results of scientific experiments in the form of scientific theories – which describe not what is particular in reality but rather what is general

Ontologies need to exploit the evolutionary path to convergence created by science

High quality shared ontologies build communities

NIH, FDA trend to consolidate ontology-based standards for the communication and processing of biomedical data.

caBIG / NECTAR / BIRN / BRIDG ...

http://obo.sourceforge.net

http://www.geneontology.org/

The Methodology of Annotations

GO employs scientific curators, who use experimental observations reported in the biomedical literature to link gene products with GO terms in annotations.

This gene product exercises this function, in this part of the cell, leading to these biological processes

The Methodology of Annotations

This process of annotating literature leads to improvements and extensions of the ontology, which in turn leads to better annotations

This institutes a virtuous cycle of improvement in the quality and reach of both future annotations and the ontology itself.

Annotations + ontology taken together yield a slowly growing computer-interpretable map of biological reality.

The OBO The OBO FoundryFoundry

A subset of OBO ontologies, whose developers have agreed in advance to accept a common set of principles designed to ensure

– intelligibility to biologists (curators, annotators, users)

– formal robustness – stability– compatibility– interoperability – support for logic-based reasoning

The OBO FoundryThe OBO Foundry

Custodians

•Michael Ashburner (Cambridge)•Suzanna Lewis (Berkeley)•Barry Smith (Buffalo/Saarbrücken)


A collaborative experiment

participants have agreed in advance to a growing set of principles specifying best practices in ontology developmentdesigned to guarantee interoperability of ontologies from the very start


The developers of each ontology commit to its maintenance in light of scientific advance, and to soliciting community feedback for its improvement. They commit to working with other Foundry members to ensure that, for any particular domain, there is community convergence on a single reference ontology.


Initial Candidate Members of the OBO Foundry

– GO Gene Ontology– CL Cell Ontology– SO Sequence Ontology– ChEBI Chemical Ontology – PATO Phenotype Ontology– FuGO Functional Genomics Investigation

Ontology– FMA Foundational Model of Anatomy– RO Relation Ontology


Under development – Disease Ontology– NCI Thesaurus– Mammalian Phenotype Ontology – OBO-UBO / Ontology of Biomedical Reality – Organism (Species) Ontology– Plant Trait Ontology– Protein Ontology– RnaO RNA Ontology


Considered for development

– Environment Ontology– Behavior Ontology– Biomedical Image Ontology– Clinical Trial Ontology


CRITERIA

The OBO FoundryThe OBO FoundryThe OBO FoundryThe OBO Foundry

The ontology is open and available to be used by all.

The developers of the ontology agree in advance to collaborate with developers of other OBO Foundry ontology where domains overlap.

The ontology is in, or can be instantiated in, a common formal language.

The ontology possesses a unique identifier space within OBO.

The ontology provider has procedures for identifying distinct successive versions.

The ontology includes textual definitions for all terms.

CRITERIA


The ontology has a clearly specified and clearly delineated content.

The ontology is well-documented.

The ontology has a plurality of independent users.

CRITERIA


The ontology uses relations which are unambiguously defined following the pattern of definitions laid down in the OBO Relation Ontology.*

*Genome Biology 2005, 6:R46

CRITERIA


CRITERIA

Further criteria will be added over time in order to bring about a gradual improvement in the quality of the ontologies in the Foundry

The OBO FoundryThe OBO FoundryThe OBO FoundryThe OBO Foundry

A reference ontology

is analogous to a scientific theory; it seeks to optimize representational adequacy to its subject matter to the maximal degree that is compatible with the constraints of computational usefulness.

An application ontology

is comparable to an engineering artifact such as a software tool. It is constructed for a specific practical purpose.Examples:

National Cancer Institute Thesaurus FuGO Functional Genomics

Investigation Ontology

Reference Ontology vs. Application Ontology

Currently, application ontologies are often built afresh for each new task; commonly introducing not only idiosyncrasies of format or logic, but also simplifications or distortions of their subject-matters. To solve this problem application ontology development should take place always against the background of a formally robust reference ontology framework

Advantages of the methodology of shared coherently defined

ontologies• promotes quality assurance (better

coding)• guarantees automatic reasoning across

ontologies and across data at different granularities

• yields direct connection to temporally indexed instance data

Advantages of the methodology of shared coherently defined

ontologies

We know that high-quality ontologies can help in creating better mappings e.g. between human and model organism phenotypes

S Zhang, O Bodenreider, “Alignment of Multiple Ontologies of Anatomy: Deriving Indirect Mappings from Direct Mappings to a Reference Ontology”, AMIA 2005

Advantages of the methodology of shared coherently defined ontologies

once the interoperable gold standard reference ontologies are there, it will make sense to reformulate parts of existing incompatible terminologies (e.g. in UMLS) in terms of the standard ontologies in order to achieve greater domain coverage and alignment of different but veridical views. Thus not everything that was done in the past turns out to be a waste.

Goal: to create a family of gold standard reference ontologies upon which terminologies developed for specific applications can draw


Goal: to introduce the scientific method into ontology development:– all Foundry ontologies must be constantly

updated in light of scientific advance– all Foundry ontology developers must work

with all other Foundry ontology developers in a spirit of scientific collaboration


Goal: to replace the current policy of ad hoc

creation of new database schemas by each clinical research group by providing reference ontologies in terms of which database schemas can be defined


Goal: to introduce some of the features of scientific peer review into biomedical ontology development


Goal:to create controlled vocabularies for use by clinical trial banks, clinical guidelines bodies, scientific journals, ...


Goal:to create an evolving map-like representation of the entire domain of biological reality


GO’s three ontologies

molecular function

cellular component

biological process

cell (types)

molecular function

(GO)

species

molecular process

cellular anatom

y

anatomy(fly, fish,

human...)

cellularphysiology

organism-levelphysiology

ChEBI,Sequence,

RNA ...

cell (types)

molecular function

(GO)

species

molecular process

cellular anatom

y

anatomy(fly, fish, human...)

cellularphysiology


ChEBI,Sequence,

RNA ...

normal(functionings)

pathophysiology(disease)

pathoanatomy(fly, fish, human ...)

pathological(malfunctionings)

cell (types)

molecular function

(GO)

species

molecular process

cellular anatom

y(GO)


cellularphysiology


ChEBI,Sequence,

RNA ...



cell (types)

molecular function

(GO)

species

molecular process

cellular anatom

y


cellularphysiology


ChEBI,Sequence,

RNA ...



phenotype

cell (types)

molecular function

(GO)

species

molecular process

cellular anatom

y


cellularphysiology


ChEBI,Sequence,

RNA ...



phenotype

investigation(FuGO)

First step

Alignment of OBO Foundry ontologies through a common system of formally defined relations in the OBO Relation Ontology

See “Relations in Biomedical Ontologies”, Genome Biology Apr. 2005

Judith Blake:

“The use of bio-ontologies … ensures consistency of data curation, supports extensive data integration, and enables robust exchange of information between heterogeneous informatics systems. .. ontologies … formally define relationships between the concepts.”

"Gene Ontology: Tool for the Unification of Biology"

an ontology "comprises a set of well-defined terms with well-defined relationships" (Ashburner et al., 2000, p. 27)

is_a (sensu UMLS)A is_a B =def

‘A ’ is narrower in meaning than ‘B ’

grows out of the heritage of dictionaries

(which ignore the basic distinction between types and instances)

is_acongenital absent nipple is_a nipplecancer documentation is_a cancerdisease prevention is_a diseaseNazism is_a social science

is_a (sensu logic)A is_a B =def

For all x, if x instance_of A then x instance_of B

cell division is_a biological process

adult is_a child ???

Two kinds of entitiesoccurrents (processes, events,

happenings)cell division, ovulation, death

continuants (objects, qualities, ...)cell, ovum, organism, temperature of organism, ...

is_a (for occurrents)A is_a B =def

For all x, if x instance_of A then x instance_of B

cell division is_a biological process

is_a (for continuants)A is_a B =def

For all x, t if x instance_of A at t then x instance_of B at t

abnormal cell is_a celladult human is_a humanbut not: adult is_a child

Part_of as a relation between types is more problematic than is standardly supposed

heart part_of human being ?human heart part_of human being ?human being has_part human testis ?human testis part_of human being ?

two kinds of parthood

1. between instances:Mary’s heart part_of Marythis nucleus part_of this cell

2. between typeshuman heart part_of humancell nucleus part_of cell

Definition of part_of as a relation between types

A part_of B =Def all instances of A are instance-level parts of some instance of B

ALL–SOME STRUCTURE

part_of (for occurrents)A part_of B =Def

For all x, if x instance_of A then there is some y, y instance_of B and x part_of ywhere ‘part_of’ is the instance-level part relation

part_of (for continuants)A part_of B =def.

For all x, t if x instance_of A at t then there is some y, y instance_of B at t and x part_of y

where ‘part_of’ is the instance-level part relation

ALL-SOME STRUCTURE

How to use the OBO Relation OntologyOntologies are representations of types and

of the relations between typesThe definitions of these relations involve

reference to times and instances, but these references are washed out when we get to the assertions (edges) in the ontology

But curators should still be aware of the underlying definitions when formulating such assertions

part_of (for occurrents)A part_of B =Def

For all x, if x instance_of A then there is some y, y instance_of B and x part_of ywhere ‘part_of’ is the instance-level part relation

A part_of B, B part_of C ...The all-some structure of such

definitions allowscascading of inferences (true path

rule)(i) within ontologies(ii) between ontologies(iii) between ontologies and repositories of instance-data

Strengthened true path ruleWhichever A you choose, the instance of

B of which it is a part will be included in some C, which will include as part also the A with which you began

The same principle applies to the other relations in the OBO-RO:

located_at, transformation_of, derived_from, adjacent_to, etc.

Kinds of relationsBetween types:

– is_a, part_of, ...

Between an instance and a type– this explosion instance_of the type

explosion

Between instances:– Mary’s heart part_of Mary

In every ontologysome terms and some relations are primitive = they cannot be defined (on pain of infinite regress)

Examples of primitive relations:– identity– instantiation– (instance-level) part_of– (instance-level)

continuous_with

Fiat and bona fide boundaries

Continuity

Attachment

Adjacency

everything here is an independent continuant

structures vs. formations = bona fide vs. fiat boundaries

Modes of Connection

The body is a highly connected entity.

Exceptions: cells floating free in blood.

Modes of Connection

Modes of connection:attached_to (muscle to bone) synapsed_with (nerve to nerve, nerve to muscle)

continuous_with (= share a fiat boundary)

articular eminencearticular (glenoid)fossa

ANTERIOR

Attachment, location, containment

Containment involves relation to a hole or cavity

1: cavity2: tunnel, conduit (artery)3: mouth; a snail’s shell

Fiat vs. Bona Fide Boundaries

Fiat boundary Physical boundary

Double Hole Structure

Medium (filling the environing hole)

Tenant (occupying the central hole)

Retainer (a boundary of some surrounding structure)

head of condyle

neck of condyle

fossa

fiat boundary

THE TEMPOROMANDIBULAR JOINTTHE TEMPOROMANDIBULAR JOINT

continuous_with(a relation between instances which

share a fiat boundary)

is always symmetric:

if x continuous_with y , then y continuous_with x

continuous_with(relation between types)

A continuous_with B =Def.

for all x, if x instance-of A then there is some y such that y instance_of B and x continuous_with y

continuous_with is not always symmetric

Consider lymph node and lymphatic vessel:

Each lymph node is continuous with some lymphatic vessel, but there are lymphatic vessels (e.g. lymphs and lymphatic trunks) which are not continuous with any lymph nodes

Adjacent_toas a relation between types

is not symmetric

Considerseminal vesicle adjacent_to urinary bladder

Not: urinary bladder adjacent_to seminal vesicle

instance levelthis nucleus is adjacent to this

cytoplasmimplies:

this cytoplasm is adjacent to this nucleus

type levelnucleus adjacent_to cytoplasmNot: cytoplasm adjacent_to nucleus

ApplicationsExpectations of symmetry e.g. for

protein-protein interactions may hold only at the instance level

if A interacts with B, it does not follow that B interacts with A

if A is expressed simultaneously with B, it does not follow that B is expressed simultaneously with A

c at t1

C

c at t

C1

time

same instance

transformation_of

pre-RNA mature RNA

adultchild

transformation_of

A transformation_of B =Def. Every instance of A was at some earlier time an instance of B

adult transformation_of child

C

c at t c at t1

C1

tumor development

C

c at t

C1

c1 at t1

C'

c' at t

time

instances

zygote derives_fromovumsperm

derives_from

two continuants fuse to form a new continuant

C

c at t

C1

c1 at t1

C'

c' at t fusion

one initial continuant is replaced by two successor continuants

C

c at t

C1

c1 at t1

C2

c1 at t1

fission

one continuant detaches itself from an initial continuant, which itself continues to exist

C

c at t c at t1

C1

c1 at t

budding

one continuant absorbs a second continuant while itself continuing to exist

C

c at t

c at t1

C'

c' at t capture

A suite of defined relations between typesFoundation

al is_apart_of

Spatial located_incontained_inadjacent_to

Temporal transformation_ofderives_frompreceded_by

Participation

has_participanthas_agent

To be added to the Relation Ontology

lacks (between an instance and a type, e.g. this fly lacks wings)

dependent_on (between a dependent entity and its carrier or bearer)

quality_of (between a dependent and an independent continuant)

functioning_of (between a process and an independent continuant)

Low Hanging Fruit

Ontologies should include only those relational assertions which hold universally (= have the ALL-SOME form)

Often, order will matter here:We can include

adult transformation_of childbut not

child transforms_into adult

The Gene Ontology


molecular functions

cellular components

biological processes

When a gene is identified

three types of questions need to be addressed:

1. Where is it located in the cell? 2. What functions does it have on the

molecular level? 3. To what biological processes do these

functions contribute?

Three granularities:

Cellular (for components)Molecular (for functions)Organ + organism (for processes)

GO has cells

but it does not include terms for molecules or organisms within any of its three ontologiesexcept e.g. GO:0018995 host=Def. Any organism in which another organism spends part or all of its life cycle

Are the relations between functions and processes a matter of granularity?

Molecular activities are the ‘building blocks’ of biological processes ?

But they are not allowed to be represented in GO as parts of biological processes


molecular functions

cellular components

biological processes

What does “function” mean?

an entity has a biological function if and only if it is part of an organism and has a disposition to act reliably in such a way as to contribute to the organism’s survival

the function is this disposition

Improved version

an entity has a biological function if and only if it is part of an organism and has a disposition to act reliably in such a way as to contribute to the organism’s realization of the canonical life plan for an organism of that type

This canonical life plan might include

canonical embryological development

canonical growthcanonical reproductioncanonical agingcanonical death

The function of the heart is to pump blood

Not every activity (process) in an organism is the exercise of a function – there are – mal functionings– side-effects (heart beating)– accidents (external

interference)– background stochastic activity

Kidney

Nephron

Functional Segments

Functions

FunctionsThis is a screwdriverThis is a good screwdriverThis is a broken screwdriver

This is a heartThis is a healthy heartThis is an unhealthy heart

Functions are associated with certain characteristic process shapes

Screwdriver: rotates and simultaneously moves forward simultaneously transferring torque from hand and arm to screw

Heart: performs a contracting movement inwards and an expanding movement outwards

Not functioning at all

leads to death, modulo internal factors:

plasticity redundancy (2 kidneys)criticality of the system involved

external factors:prosthesis (dialysis machines, oxygen tent)special environmentsassistance from other organisms

What clinical medicine is for

to eliminate malfunctioning by fixing broken body parts(or to prevent the appearance of malfunctioning by intervening e.g. at the molecular level)

Hypothesis: there are no ‘bad’ functions

It is not the function of an oncogene to cause cancer Oncogenes were in every case proto-oncogenes with functions of their ownThey become oncogenes because of bad (non-prototypical) environments

Is there an exception for molecular functions?

Does this apply only to functions on biological levels of granularity

(= levels of granularity coarser than the molecule) ?

If pathology is the deviation from (normal) functioning, does it make sense to talk of a pathological molecule?

(Pathologically functioning molecule vs. pathologically structured molecule)

Is there an exception for molecular functions?

A molecular function is a propensity of a gene product instance to perform actions on the molecular level of granularity. Hypothesis 1: these actions must be reliably such as to contribute to biological processes.Hypothesis 2: these actions must be reliably such as to contribute to the organism’s realization of the canonical life plan for an organism of that type.

The Gene Ontology

is a canonical ontology – it represents only what is normal in the realm of molecular functioning

The GO is a canonical representation

“The Gene Ontology is a computational representation of the ways in which gene products normally function in the biological realm”

Nucl. Acids Res. 2006: 34.

The FMA is a canonical representation

It is a computational representation of types and relations between types deduced from the qualitative observations of the normal human body, which have been refined and sanctioned by successive generations of anatomists and presented in textbooks and atlases of structural anatomy.

The importance of pathways (successive causality)

Each stage in the history of a disease presupposes the earlier stages

Therefore need to reason across time, tracking the order of events in time, using relations such as derives_from, transformation_of ...

Need pathway ontologies on every level of granularity

The importance of granularity (simultaneous causality)

Networks are continuantsAt any given time there are networks existing

in the organism at different levels of granularity

Changes in one cause simultaneous changes in all the others

(Compare Boyle’s law: a rise in temperature causes a simultaneous increase in pressure)

The Granularity Gulf

most existing data-sources are of fixed, single granularity

many (all?) clinical phenomena cross granularities

Therefore need to reason across time, tracking the order of events in time

Good ontologies require:

consistent use of terms, supported by logically coherent (non-circular) definitions, in equivalent human-readable and computable formats

coherent shared treatment of relations to allow cascading inference both within and between ontologies

Three fundamental dichotomies

• continuants vs. occurrents• dependent vs. independent • types vs. instances

ONTOLOGIES AREREPRESENTATIONS OF TYPES

aka kinds, universals, categories, species, genera, ...

Continuants (aka endurants)– have continuous existence in time– preserve their identity through

change– exist in toto whenever they exist at

all

Occurrents (aka processes)– have temporal parts– unfold themselves in successive

phases– exist only in their phases

You are a continuant

Your life is an occurrent

You are 3-dimensional

Your life is 4-dimensional

Dependent entities

require independent continuants as their bearers

There is no run without a runnerThere is no grin without a cat

Dependent vs. independent continuants

Independent continuants (organisms, cells, molecules, environments)

Dependent continuants (qualities, shapes, roles, propensities, functions)

All occurrents are dependent entities

They are dependent on those independent continuants which are their participants (agents, patients, media ...)

Top-Level Ontology

ContinuantOccurrent

(always dependent on one or more

independent continuants)

IndependentContinuant

DependentContinuant

= A representation of top-level types

Continuant Occurrent


DependentContinuant

cell component

biological process

molecular function

Top-Level Ontology

Continuant Occurrent


DependentContinuant

Functioning

Side-Effect, Stochastic Process, ...

Function

Top-Level OntologyContinuant Occurrent


DependentContinuant

Functioning Side-Effect, Stochastic Process, ...

Function

Top-Level OntologyContinuant Occurrent


DependentContinuant

Quality Function Spatial Region

Functioning Side-Effect, Stochastic Process, ...

instances (in space and time)

Smith B, Ceusters W, Kumar A, Rosse C. On Carcinomas and Other Pathological Entities, Comp Functional Genomics, Apr. 2006

everything here is an independent continuant

Functions, etc.

Some dependent continuants are realizable

expression of a geneapplication of a therapycourse of a diseaseexecution of an algorithmrealization of a protocol

Functions vs Functionings

the function of your heart = to pump blood in your body

this function is realized in processes of pumping blood

not all functions are realized (consider the function of this sperm ...)

Concepts

Biomedical ontology integration will never be achieved through integration of meanings or concepts

The problem is precisely that different user communities use different concepts

Concepts are in your head and will change as your understanding changes

ConceptsOntologies represent types: not

concepts, meanings, ideas ...Types exist, with their instances, in

objective reality– including types of image, of imaging

process, of brain region, of clinical procedure, etc.

Rules on typesDon’t confuse types with wordsDon’t confuse types with conceptsDon’t confuse types with ways of

getting to know typesDon’t confuse types with ways of

talking about typesDon’t confuses types with data about

types

Some other simple rules for high quality ontologies

Univocity Terms should have the same meanings

on every occasion of use.They should refer to the same kinds of

entities in realityBasic ontological relations such as is_a

and part_of should be used in the same way by all ontologies

Positivity

Complements of types are not themselves types. Hence terms such as

non-mammal non-membrane other metalworker in New Zealand

do not designate types in reality

Ontology of types logic of termsThere are no conjunctive and

disjunctive types:

anatomic structure, system, or substance

musculoskeletal and connective tissue disorder

rheumatism, excluding the back

ObjectivityWhich types exist in reality is not a

function of our knowledge.Terms such as

unknownunclassifiedunlocalizedarthropathies not otherwise specified

do not designate types in reality.

Keep Epistemology Separate from OntologyIf you want to say that

We do not know where A’s are located

do not invent a new class of A’s with unknown locations(A well-constructed ontology should grow linearly; it should not need to delete classes or relations because of increases in knowledge)

Syntactic SeparatenessDo not confuse sentences with terms

If you want to say

I surmise that this is a case of pneumonia

do not invent a new class of surmised pneumonias

Single Inheritance

No kind in a classificatory hierarchy should have more than one is_a parent on the immediate higher level

Multiple Inheritance

thing

car

blue thing

blue car

is_a is_a


is a source of errorsencourages lazinessserves as obstacle to integration with

neighboring ontologieshampers use of Aristotelian methodology

for defining terms


thing

car

blue thing

blue car

is_a1 is_a2

is_a Overloading

The success of ontology alignment demands that ontological relations (is_a, part_of, ...) have the same meanings in the different ontologies to be aligned.

Example: is_a is pressed into service by the GO to express location

is-located-at and similar relations are expressed by creating special compound terms using:

site of …… within …… in …extrinsic to …

yielding associated errors

e.g. errors with ‘within’lytic vacuole within a protein storage

vacuole

lytic vacuole within a protein storage vacuole is-a protein storage vacuole

Compare:embryo within a uterus is-a uterus

similar problems with part_of

extrinsic to membrane part_of membrane

CompositionalityThe meanings of compound terms

should be determined 1. by the meanings of component terms

together with2. the rules governing syntax

Why do we need rules/standards for good ontology?

Ontologies must be intelligible both to humans (for annotation and curation) and to machines (for reasoning and error-checking): the lack of rules for classification leads to human error and blocks automatic reasoning and error-checking

Intuitive rules facilitate training of curators and annotators

Common rules allow alignment with other ontologies

When we annotate the record of an experiment

we use terms representing types to capture what we learn about:– this experiment (instance), performed here

and now, in this laboratory– the instances experimented upon

These instances are typical = they are representatives of types – of experiment (described in FuGO)– of gene product molecules, molecular

functions, cellular components, biological processes (described in GO)

Experimental records

document a variety of instances (particular real-world examples or cases), ranging from instances of gene products (including individual molecules) to instances of biochemical processes, molecular functions, and cellular locations

Experimental records

provide evidence that gene products of given types have molecular functions of given types by documenting occurrences in the real world that involve corresponding instances of functioning.

They document the existence of real-world molecules that have the potential to execute (carry out, realize, perform) the types of molecular functions that are involved in these occurrences.

Motivation: To capture realityInferences and decisions we make are

based upon what we know of reality.An ontology is a computable

representation of biological reality, which is designed to enable a computer to reason over the data we collect about this reality in (some of) the ways that we do.

ontology and the future of biomedical research barry smith

Documents

disease slide

fido slide

agent slide

registries slide

querying slide

mesh national socialism

beer ontology

formal ontology