introduction to databases: from data to knowledge bases instructors: bertram ludaescher kai lin...

Introduction to Databases:Introduction to Databases:From Data to Knowledge BasesFrom Data to Knowledge BasesIntroduction to Databases:Introduction to Databases:From Data to Knowledge BasesFrom Data to Knowledge Bases

Instructors:

Bertram LudaescherKai Lin

Instructors:

Bertram LudaescherKai Lin

2Introduction to KR, B. Ludaescher & K. Lin

Overview• 08:30-9:30 Introduction to KR (1h)• 9:30 – 9:45 BREAK (15’)• 9:45 -11:20 Intro to KR (1h45’)• 11:20-11:50 Demos (30’)• 11:50-13:15 LUNCH (1h25’)

• Demonstrations/Hands-on (~30’)• Ontology-enabled data integration• Concept map creation tool• Ontology creation tool


The Problem: Scientific Data Integration

or: … from Questions to Queries …


Ontology Cheat Sheet (1/2)• What is an ontology? An ontology usually …

– specifies a theoryspecifies a theory (a set of logic modelsmodels) by …– definingdefining and relatingrelating …– conceptsconcepts representing features of a domain of interest

• Also overloaded (sloppy) for:– Controlled vocabularies– Database schema (relational, XML Schema/DTD, …)– Conceptual schema (ER, UML, … )– Thesauri (synonyms, broader term/narrower term)– Taxonomies (classifications)– Informal/semi-formal knowledge representations

• “Concept spaces”, “concept maps”• Labeled graphs / semantic networks (RDF)

– Formal ontologies, e.g., in [Description] Logic (OWL)• “formalization of a specification” constrains possible interpretation of terms


Ontology Cheat Sheet (2/2)• What are ontologies used for?

– Conceptual models of a domain or application, (communication means, system design, …)

– Classification of …• concepts (taxonomy) and • data/object instances through classes

– Analysis of ontologies e.g.• Graph queries (reachability, path queries, …)• Reasoning (concept subsumption, consistency checking, …)

– Targets for semantic data registration– Conceptual indexes and views for

• searching,• browsing, • querying, and • integration of registered data


Ontologies as Metadata++

Ontologies = Smarter Metadata

TM


Smarter (Meta)data I: Logical Data Views

Source: NADAM Team(Boyan Brodaric et al.)

Adoption of a standard (meta)data model => wrap data sets into unified virtual views


Smarter Metadata II: Multihierarchical Rock Classification for “Thematic Queries” (GSC) –– or: Taxonomies are not only for

biologists ...

Composition

Genesis

Fabric

Texture

“smart discovery & querying” via multiple, independent concept hierarchies (controlled vocabularies)• data at different description levels can be found and processed


Biomedical InformaticsResearch Networkhttp://nbirn.net

Biomedical InformaticsResearch Networkhttp://nbirn.net

Smarter Metadata III: Source Contextualization & Ontology Refinement

The next frontier: Capturing Knowledge about Dynamic Processes

“Process Ontologies”


Ontology-Enabled Application Example:Geologic Map Integration

Show formations where AGE = ‘Paleozic’

(without age ontology)


(without age ontology)


(with age ontology)


(with age ontology)

+/- a few hundred million years

domainknowledge

domainknowledge

Knowledge r

epresentatio

n

AGE ONTOLOGY

NevadaNevada


Integrated querying of multiple datasets via different “ontologies” (conceptual

views)


Querying by Geologic Age …


Querying by Geologic Age: Result


Querying by Chemical Composition …


Querying by Chemical Composition: Results

DO know: It’s NOT there!

DON’T know! (not registered)

Note the fine differences in

shades of gray:

OK – we got to work on the color coding ;-)

OK – we got to work on the color coding ;-)

?

!


Querying w/ British Rock Classification

Uses a GSC BRC inter-ontology articulation mapping Uses a GSC BRC inter-ontology articulation mapping


British Rock Classification Query: Results

Uses a GSC BRC inter-ontology articulation mapping Uses a GSC BRC inter-ontology articulation mapping


Different views on State Geological Maps


The Query: Show sedimentary rocksThe Puzzle: Find the 17 differences in the

results… but first: what states are we looking at?but first: what states are we looking at?


Sedimentary Rocks: BGS Ontology


Sedimentary Rocks: GSC Ontology


Differing Conceptual Views: Why?• We are looking at the same datasets – why do

they look different?– Different rock classifications (GSC, BGS) are used as

“targets” for registering data to– Not every rock name/rock type found in the raw data

is found in both classifications– The mapping (“articulation”) between the

classifications is an approximation only

• Yet: having “conceptual views” (even if different) on the data really seems like a good idea…


Geologic Map Integration

• Given: – Geologic maps from different state geological

surveys (shapefiles w/ different data schemas)– Different ontologies:

• Geologic age ontology• Rock classification ontologies:

– Multiple hierarchies (chemical, fabric, texture, genesis) from Geological Survey of Canada (GSC)

– Single hierarchy from British Geological Survey (BGS)

• Problem:– Support uniform queries using different

ontologies– Support registration w/ ontology A, querying

w/ ontology B


A Multi-Hierarchical Rock Classification “Ontology” (really:Taxonomy)

Composition

Genesis

Fabric

Texture


Implementation in OWL: Not only “for the machine” …


Demonstration ofOntology-enabled Map Integration (OMI) v2

Data

Data

Data

Data

ontology A

ontology C

ontology B

Ontology enabled Map Integrator {A,B}

Application (B)

Application (C)

“Semantic Registration”

Data sets Ontologies Applications


Ontology Mapping: Overview

• Align ontologies• Integrate data sets which are registered to

different ontologies• Query data sets through different ontologies

Data set 1

Data set 2

Ontology 1

Ontology 2

register

register

Ontology mappings queries


Geology Workbench: Initial State

click on Ontologies click on Datasets click on Applications

An Ontology-based Mediator


Geology Workbench: Uploading Ontologies

click on Ontology SubmissionChoose an OWL file to uploadClick to check its detail Name SpaceCan be used to import this

ontology into others


Geology Workbench: Data (to Ontology!) Registration

Step 1: Choose Classes

Click on Submission Data set name

Select a shapefile

Choose an ontology


Geology Workbench: Data RegistrationStep 2: Choose Columns for Selected Classes

AREA

PERIMETER

AZ_1000

AZ_1000_ID

GEO

PERIOD

ABBREV

DESCR

D_SYMBOL

P_SYMBOL

It contains information about geologic age


Geology Workbench: Data RegistrationStep 3: Resolve Mismatches

Two terms arenot matched anyontology terms

Manually mappingalgonkian intothe ontology


Geology Workbench: Ontology-enabled Map Integrator

Click on the nameChoose interesting

Classes

All areas with the age Paleozoic


Geology Workbench: Change Ontology

Submit a mapping

Ontology mappingbetween British Rock

Classification and CanadianRock Classification

Switch from Canadian Rock Classification to

British Rock Classification

Run it New query interface


Ontology Repository• Accept user-defined ontologies in OWL• Any ontology saved in the system or accessible by can be

imported into another user-defined ontology ( inter-ontology references)

• Provide tool to browse the ontologies in the repository

……………..<owl:Ontology> <owl:imports rdf:resource= "http://compute5.sdsc.geongrid.org:8080/workbench/jsp/ontologies/genesis.owl" /></owl:Ontology>…………….<owl:Class rdf:ID="Ultramafite"> <rdfs:subClassOf rdf:resource="#Ultramafic"/> <rdfs:subClassOf rdf:resource= "http://compute5.sdsc.geongrid.org:8080/workbench/jsp/ontologies/genesis.owl#Igneous"></owl:Class>……………..

composition.owl


Ontology-Enabled Map Integration: Where do we stand?

The simple case (done) : ontologies contain only the subclass relation

More complicate cases (coming soon) : ontologies contain classes with attributes ontologies with constraints in Description Logic

Implementation:• v1,v2 prototypes: detail-level registration to ontology• v3 (portal): item-level registration to ontology


Current Ontology Registration (Item-level) v3

Domain Knowledge Ontologies

Domain Knowledge Ontologies

ArizonaArizona


GEON Search: Concept-based Querying


GEONmiddleware

System Overview

myOntology.owl myDataset.foo

metadatametadata

User Access (via Portal)User Access (via Portal)

Gazetteer, DLESE, …

Geologic Age, Chronos, …

external services

GEONsearchGEONsearch

Search condition(s)spatial temporal concept

LogLog

GEONworkbench GEONworkbench

GEON Workspace

(user)

User actionsadd delete manipulate

GEON Catalog

ResourceRegistrationResourceRegistration

SRB

Client Access (via web services)Client Access (via web services)

Other distributed apps Kepler, DLESE, …


Introduction to Knowledge Representation and Ontologies


Complex Multiple-Worlds Mediation and XML

• XML is Syntax– DTDs talk about element nesting– XML Schema schemas give you data types – need anything else? => write comments!

• Domain Semantics is complex:– implicit assumptions, hidden semantics sources seem unrelated to the non-expert

• Need Structure and Semantics beyond XML trees! employ richer OO models make domain semantics and “glue knowledge” explicit use ontologies to fix terminology and conceptualization avoid ambiguities by using formal semantics

XML-Based vs. Model-Based Mediation

Raw DataRaw DataRaw Data

IF THEN IF THEN IF THEN

LogicalDomainConstraints

Integrated-CM :=

CM-QL(Src1-CM,...)

Integrated-CM :=

CM-QL(Src1-CM,...)

. . ....

....

........ (XML)Objects

Conceptual Models

XMLElements

XML Models

C2 C3

C1

R

Classes,Relations,is-a, has-a, ...

Ontologies

DMs, PMs

Ontologies

DMs, PMs

Integrated-DTD :=

XQuery(Src1-DTD,...)

Integrated-DTD :=

XQuery(Src1-DTD,...)

No DomainConstraints

A = (B*|C),DB = ...

Structural Constraints (DTDs),Parent, Child, Sibling, ...

CM ~ {Descr.Logic, ER, UML, RDF/XML(-Schema), …} CM-QL ~ {F-Logic, OWL, …}


Knowledge Representation:Relating Theory to the World via Formal Models

Source: John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations

““All models are wrong, but some models are useful!”All models are wrong, but some models are useful!”


What is an ontology??


Glossary (wordreference.com)• ontology noun

1 (Philosophy) the branch of metaphysics that deals with the nature of being2 (Logic) the set of entities presupposed by a theory

• taxonomy noun1 a the branch of biology concerned with the classification of organisms into groups based on similarities of structure, origin, etc.b the practice of arranging organisms in this way2 the science or practice of classification [ETYMOLOGY: 19th Century: from French

taxonomie, from Greek taxis order + -nomy]

• thesaurus noun(plural: -ruses, -ri [-raı])1 a book containing systematized lists of synonyms and related words2 a dictionary of selected words or topics3 (rare)

a treasury[ETYMOLOGY: 18th Century: from Latin, Greek: treasure]


Glossary (wordreference.com)• concept noun1 an idea, esp. an abstract ideaexample: the concepts of biology2 (Philosophy) a general idea or notion that corresponds to some class of entities and that consists of the characteristic or essential features of the class3 (Philosophy) a the conjunction of all the characteristic features of something b a theoretical construct within some theory c a directly intuited object of thought d the meaning of a predicate4 [modifier] (of a product, esp. a car) created as an exercise to demonstrate the technical skills and imagination of the designers, and not intended for mass production or sale[ETYMOLOGY: 16th Century: from Latin conceptum something received or conceived, from concipere to take in, conceive]

• contingent adjective1 [when postpositive, often foll by on or upon] dependent on events, conditions, etc., not yet known; conditional2 (Logic) (of a proposition) true under certain conditions, false under others; not necessary3 (in systemic grammar) denoting contingency (sense 4)4 (Metaphysics) (of some being) existing only as a matter of fact; not necessarily existing5 happening by chance or without known cause; accidental6 that may or may not happen; uncertain

• glossary noun (plural: -ries); an alphabetical list of terms peculiar to a field of knowledge with definitions or explanations. Sometimes called: gloss[ETYMOLOGY: 14th Century: from Late Latin glossarium; see gloss2]


1st Attempt: Ontologies in CS

• An ontology is ...– an explicit specification of a conceptualization [Gruber93]

– a shared understanding of some domain of interest [Uschold, Gruninger96]

• Different aspects:– a formal specification (reasoning and “execution”)– ... of a conceptualisation of a domain (community)– ... of some part of the world of interest (application, science

domain)

• Provides:– A common vocabulary of terms– Some specification of the meaning of the terms (semantics)– A shared “understanding” for people and machines


Ontology as a philosophical discipline

• Ontology as a philosophical discipline, which deals with the nature and the organization of reality:– Ontology as such is usually contrasted with Epistemology,

which deals with the nature and sources of our knowledge [a.k.a. Theory of Knowledge]. Aristotle defined Ontology as the science of being as such: unlike the special sciences, each of which investigates a class of beings and their determinations, Ontology regards all the species of being qua being and the attributes which belong to it qua being" (Aristotle, Metaphysics, IV, 1).

• In this sense Ontology tries to answer to the question: What is being? What exists? – the nature of being, not an enumeration of “stuff” around us…


Some different uses of the word “Ontology” [Guarino’95]

1. Ontology as a philosophical discipline2. Ontology as a an informal conceptual system3. Ontology as a formal semantic account4. Ontology as a specification of a “conceptualization”5. Ontology as a representation of a conceptual systemvia a logical theory

5.1 characterized by specific formal properties5.2 characterized only by its specific purposes

6. Ontology as the vocabulary used by a logical theory7. Ontology as a (meta-level) specification of a logical

theory

http://ontology.ip.rm.cnr.it/Papers/KBKS95.pdf


Ontologies vs Conceptualizations• Given a logical language L ...

– ... a conceptualization is a set of models of L which describes the admittable (intended) interpretations of its non-logical symbols (the vocabulary)

– ... an ontology is a (possibly incomplete) axiomatization of a conceptualization.

conceptualization conceptualization C(L)C(L)

ontologyontology

set of all models M(L)set of all models M(L)logiclogictheoriestheories(consistent sets of (consistent sets of sentences; closed undersentences; closed underlogical consequence)logical consequence)

[Guarino96]http://www-ksl.stanford.edu/KR96/Guarino-What/P003.html


Ontologies vs Knowledge Bases• An ontology is a particular KB, describing facts

assumed to be always true by a community of users:– in virtue of the agreed-upon meaning of the vocabulary used

(analytical knowledge):• black => not white

– ... whose truth does not descend from the meaning of the vocabulary used (non-analytical, common knowledge)

• Rome is the capital of Italy

• An arbitrary KB may describe facts which are contingently true, and relevant to a particular epistemic state:– Mr Smith’s pathology is either cirrhosis or diabetes


Formal Ontology [Guarino’96]• Theory of formal distinctions

– among things– among relations

• Basic tools– Theory of parthood

• What counts as a part of a given entity? What properties does the part relation have? Are the different kinds of parts?

– Theory of integrity• What counts as a whole? In which sense are its parts connected?

– Theory of identity• How can an entity change while keeping its identity? What are its

essential properties? Under which conditions does an entity loose its identity? Does a change of “point of view” change the identity conditions?

– Theory of dependence• Can a given entity exist alone, or does it depend on other entities?


Ontology: Definition and Scope [Sowa]

• The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D.

• An uninterpreted logic, such as predicate calculus, conceptual graphs, or KIF, is ontologically neutral. It imposes no constraints on the subject matter or the way the subject may be characterized. By itself, logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest.

http://users.bestweb.net/~sowa/ontology/index.htm


Ontology: Definition and Scope [Sowa]

• An informal ontology may be specified by a catalog of types that are either undefined or defined only by statements in a natural language. A formal ontology is specified by a collection of names for concept and relation types organized in a partial ordering by the type-subtype relation. Formal ontologies are further distinguished by the way the subtypes are distinguished from their supertypes: – an axiomatized ontology distinguishes subtypes by axioms and

definitions stated in a formal language, such as logic or some computer-oriented notation that can be translated to logic

– a prototype-based ontology distinguishes subtypes by a comparison with a typical member or prototype for each subtype.

• Large ontologies often use a mixture of definitional methods: formal axioms and definitions are used for the terms in mathematics, physics, and engineering; and prototypes are used for plants, animals, and common household items. .

http://users.bestweb.net/~sowa/ontology/index.htm


Why develop an ontology?

• To make domain assumptions explicit– Easier to change domain assumptions– Easier to understand, update, and integrate legacy

data data integration

• To separate domain knowledge from operational knowledge– Re-use domain and operational knowledge separately

• A community reference for applications• To share a consistent understanding of what

information means.[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial][Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]


What is being shared?

Metadata• Data describing the content and meaning of resources

and services.• But everyone must speak the same language…

Terminologies• Shared and common vocabularies• For search engines, agents, curators, authors and users • But everyone must mean the same thing…

Ontologies• Shared and common understanding of a domain• Essential for search, exchange and discovery Ontologies aim at sharing meaning

[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial][Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]


Origin and History• Humans require words (or at least symbols) to

communicate efficiently. The mapping of words to things is indirect. We do it by creating concepts that refer to things.

• The relation between symbols and things has been described in the form of the meaning triangle:

“Jaguar“

Concept

Ogden, C. K. & Richards, I. A. 1923. "The Meaning of Meaning." 8th Ed. New York, Harcourt, Brace & World, Inc

before: Frege, Peirce; see [Sowa 2000]

[Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial][Carole Goble, Nigel Shadbolt, Ontologies and the Grid Tutorial]


Human and machine communication

• ... MachineAgent 1

Things

HumanAgent 2

Ontology Description

MachineAgent 2

exchange symbol,e.g. via nat. language

‘‘JAGUAR“

Internalmodels

Concept

Formalmodels

exchange symbol,e.g. via protocols

MA1HA1 HA2

MA2

Symbol

commit commit

a specific domain, e.g.animals

commitcommitOntology

Formal Semantics

HumanAgent 1

MeaningTriangle

[Maedche et al., 2002]


Introduction to Description Logics

References: • F. Baader, W. Nutt. Basic Description Logics. In the

Description Logic Handbook, edited by F. Baader, D. Calvanese, D.L. McGuinness, D. Nardi, P.F. Patel-Schneider, Cambridge University Press, 2002, pages 47-100.

• Description Logics Tutorial, Ian Horrocks and Ulrike Sattler, ECAI-2002, Lyon, France, July 23rd, 2002.

• Emerging Sparrow toolkit (Bowers, Ludaescher)


Example: Description Logic

• DL definition of “Happy Father” (Example from Ian Horrocks, Ulrike Sattler, U Manchester)


Science Example: Ontology for SYNAPSE and NCMIR

Domain Map (DM)

Purkinje cells and Pyramidal cells have dendritesthat have higher-order branches that contain spines.Dendritic spines are ion (calcium) regulating components.Spines have ion binding proteins. Neurotransmissioninvolves ionic activity (release). Ion-binding proteinscontrol ion activity (propagation) in a cell. Ion-regulatingcomponents of cells affect ionic activity (release).

Domain Expert Knowledge

DM in Description Logic


Source Contextualization, Ontology Refinement

In addition to registering (“hanging off”) data relative toexisting concepts, a source may also refine the mediator’s domain map...

sources can register new concepts at the mediator ...


Some Description Logics History

• “Structured Inheritance Networks” [Brachman 1977]

• KL-ONE [Brachman, Schmolze 1985]• Core ideas:

– Building blocks: atomic concepts (unary predicates), atomic roles (binary predicates), individuals (constants)

– Constructors for building complex concepts and roles from simpler ones

– Automated inference for concept subsumption and instance classification (is-a/is-instance-of are not explicitly given by the user, but inferred from concept definitions/instance properties)


Source: Description Logics Tutorial, Ian Horrocks and Ulrike Sattler, ECAI-2002, Lyon, France, July 23rd,

2002


Knowledge Base (DL-Style)• Terminological Knowledge (TBox)

– Concept Definition (naming of concepts):

– Axiom (constraining of concepts):

=> a mediators “glue knowledge source”

• Assertional Knowledge (ABox) about Individuals– n27_img118 : Neuron=> the concrete instances/individuals of the

concepts/classes that your sources export


Example TBoxAtomic conceptsAtomic concepts = {P,F,W, M1,…} = {P,F,W, M1,…}

Base conceptsBase concepts = {P,F} = {P,F}

Defined conceptsDefined concepts = {W, M1, M2, …} = {W, M1, M2, …}

RolesRoles = { = {h1h1,,h2h2}}

Concept DefinitionConcept Definition

AxiomAxiom

where A atomic concept, where A atomic concept,

C, D complex concept expressionsC, D complex concept expressions


Example TBox• Base conceptsBase concepts = {Person, Female} = {Person, Female}

… … occur on the RHS onlyoccur on the RHS only

• Defined conceptsDefined concepts = {P, F, W, …} = {P, F, W, …}

… … occur on the LHS (& maybe RHS)occur on the LHS (& maybe RHS)

• Base interpretation Base interpretation JJ: interpret base : interpret base concepts onlyconcepts only

• Extension Extension II of of JJ:: on same domain as on same domain as J J and agrees (on base) with and agrees (on base) with JJ

• TBox TBox T T is is definitorial definitorial if every base if every base interpretation has exactly one extension interpretation has exactly one extension that is a that is a model model of of TT


Brains-On (Hands-off) SessionTM


What do we mean here?

Starting with the base interpretation of • I(Person) := “the class of persons”• I(Female) := “the class of females”

… what is the meaning of the defined concepts?… what role play the roles in this process?


And the answer is …

• atomic concept• atomic concept • concept def. w/

intersection• … plus negation• … existential restriction

• … … value restriction value restriction


Digression: “Sparrow” (Prolog) Syntax for DL

Sparrow “Grammar” and “Parser”

Example in Example in Sparrow SyntaxSparrow Syntax


Back to Reasoning with the Family ...

• concept definition: MyConcept DL-formula• concept inclusion: MyConcept DL-formula• finite set of definitions is a terminology or TBox if for

every atomic concept A there is at most one axiom whose lhs is A


Expansion of Terminologies• For acyclic T we can “unfold” concept definitions

until every defined concepts is specified in terms of primitive concepts only

the expansion of a TBox T• Example:


Reasoning in the Tableaux calculusTBox

Expansion

From this

We want to show this

In First-order (LeanTap) syntax


Reasoning Services

• Remember the distinction between evaluation a query (over a DB) vs reasoning with queries (symbolic expressions)?

• The former can be very hard, esp. for large databases and complex queries

• The latter is much harder still, even for small queries and knowledge bases, ontologies

• Specialized DL reasoners (FACT, Racer, …) better than general purpose FO reasoners


OK – enough of that jazz…

let’s look at some demos …


Tools for Editing and Processing Ontology

1. Protégé 2000 (RDF, OWL) http://protege.stanford.edu/

2. CmapTools (concept map) http://cmap.ihmc.us/

3. Java API Jena http://www.hpl.hp.com/semweb/jena.htm

OWL API http://sourceforge.net/projects/owlapi

Geology Map Integration Demo: http://geon01.sdsc.edu:8080/workbench/jsp/onto-list.jsp


ANOTHER APPLICATION OF ONTOLOGIES:

An Ontology-Driven Framework for Data Transformation in Scientific

Workflows (from DILS’04)

Shawn BowersBertram Ludäscher

San Diego Supercomputer CenterUniversity of California, San Diego


Outline• Background (SEEK Project)• Scientific Workflows• The Problem: Reusing Structurally

Incompatible Services• The Ontology-Driven Framework• Future Work


Outline• Background (SEEK Project)• Scientific Workflows• The Problem: Reusing Structurally



Science Environment for Ecological Knowledge (SEEK)

• Domain Science Driver– Ecology (LTER), biodiversity,

…

• Analysis & Modeling System– Design and execution of

ecological models and analysis

– End user focus– {application,upper}-ware

• Semantic Mediation System– Data Integration of hard-to-

relate sources and processes– Semantic Types and

Ontologies– upper middleware

• EcoGrid – Access to ecology data and

tools– {middle,under}-ware

Architecture (cf. US cyberinfrastructure, UK e-Science)

this paper


Outline• The SEEK Project• Scientific Workflows

– Focus: analysis & component integration on Focus: analysis & component integration on top of data integrationtop of data integration

• The Problem: Reusing Structurally Incompatible Services

• The Ontology-Driven Framework• Future Work


Promoter Identification in Kepler [SSDBM’03]

Promoter Identification in Kepler [SSDBM’03]

• Problems– Many components (web

serivces) are NOT designed to fit!

“The problem P that X solves is simple, and X doesn’t solve it well”

– Semantically meaningful connections are structurally incompatible

• Approach– Distinguish structural

type and semantic type– Structural type: e.g.

XML Schema– Semantic type: e.g.

OWL expressions– Exploit the (optional!)

semantic type as much as possible

• Problems– Many components (web

serivces) are NOT designed to fit!

“The problem P that X solves is simple, and X doesn’t solve it well”

– Semantically meaningful connections are structurally incompatible

• Approach– Distinguish structural

type and semantic type– Structural type: e.g.

XML Schema– Semantic type: e.g.

OWL expressions– Exploit the (optional!)

semantic type as much as possible


Service ReusabilityA scientist wishes to connect two

(independent) services

SourceServiceSourceService

TargetServiceTargetService

Ps Pt

Desired Connection


Service ReusabilityIn Ptolemy II/Kepler (and in web services), input

and output ports (message parts) have structural types (XML Schema)



Ps Pt

StructuralType Pt

StructuralType Pt

StructuralType Ps

StructuralType Ps

Desired Connection


Service ReusabilityUnless “designed to fit,” independent

services are structurally incompatible Generally, the source output type will

not be a subtype of the target input type



Ps Pt

StructuralType Pt

StructuralType Pt

StructuralType Ps

StructuralType Ps

Desired Connection

Incompatible

(⋠)


Service ReusabilityA transformation mapping () is

required to connect the services … artificially creating subtype compatibility

If such a exists, the services are “structurally feasible”



Ps Pt

StructuralType Pt

StructuralType Pt

StructuralType Ps

StructuralType Ps

Desired Connection

Incompatible

(⋠)

(Ps)(Ps) (≺)


Service ReusabilityWe can annotate services with

semantic types for discovery and interoperability of services



Ps Pt

Ontologies (OWL)Ontologies (OWL)

SemanticType Ps

SemanticType Ps

SemanticType Pt

SemanticType Pt

Desired Connection

Compatible ( )⊑


Service ReusabilityServices can be semantically

compatible, but structurally incompatible



Ps Pt

SemanticType Ps

SemanticType Ps

SemanticType Pt

SemanticType Pt

StructuralType Pt

StructuralType Pt

StructuralType Ps

StructuralType Ps

Desired Connection

Incompatible

Compatible

(⋠)

( )⊑

(Ps)(Ps) (≺)



Example Structural Types (XML)

S1

(life stage property)

S1


S2

(mortality rate for period)

S2


P1P2

P4

P3 P5

root population = (sample)*elem sample = (meas, lsp)elem meas = (cnt, acc)elem cnt = xsd:integerelem acc = xsd:doubleelem lsp = xsd:string

<population> <sample> <meas> <cnt>44,000</cnt> <acc>0.95</acc> </meas> <lsp>Eggs</lsp> </sample> …<population>

root cohortTable = (measurement)*elem measuremnt = (phase, obs)elem phase = xsd:stringelem obs = xsd:integer

<cohortTable> <measurement> <phase>Eggs</cnt> <obs>44,000</acc> </measurement>…<cohortTable>

structType(P2) structType(P3)


Example Semantic TypesPortion of SEEK measurement ontology

MeasContext

Observation EntityMeasProperty

hasContext 0:*1:1

appliesTo

hasProperty

0:*

AccuracyQualifier

EcologicalProperty

AbundanceCount

LifeStageProperty

NumericValue

SpatialLocation

hasLocation

hasCount

1:1

1:1

hasValue1:1

itemMeasured

1:*


Example Semantic TypesPortion of SEEK measurement ontology

MeasContext

Observation EntityMeasProperty

hasContext 0:*1:1

appliesTo

hasProperty

0:*

AccuracyQualifier

EcologicalProperty

AbundanceCount

LifeStageProperty

NumericValue

SpatialLocation

hasLocation

hasCount

1:1

1:1

hasValue1:1

itemMeasured

1:*

Same in OWL, a description logic standard (here, Sparrow syntax): Observation subClassOf forall hasContext/MeasContext and forall hasProperty/MeasProperty and exists itemMeasured/Entity.

MeasContext subClassOf exists appliesTo/Entity and atmost 1/appliesTo.

EcologicalProperty subClassOf Entity.

LifeStageProperty subClassOf EcologicalProperty.

AbundanceCount subClassOf EcologicalProperty and exists hasLocation/SpatialLocation and atMost 1/hasLocation and exists hasCount/NumericValue and atMost 1/hasCount.

Same in OWL, a description logic standard (here, Sparrow syntax): Observation subClassOf forall hasContext/MeasContext and forall hasProperty/MeasProperty and exists itemMeasured/Entity.

MeasContext subClassOf exists appliesTo/Entity and atmost 1/appliesTo.

EcologicalProperty subClassOf Entity.

LifeStageProperty subClassOf EcologicalProperty.

AbundanceCount subClassOf EcologicalProperty and exists hasLocation/SpatialLocation and atMost 1/hasLocation and exists hasCount/NumericValue and atMost 1/hasCount.


Example Semantic TypesSemantic types for P2 and P3

S1


S1


S2


S2


P1P2

P4

P3 P5

Observation

semType(P3)

MeasContext

hasContext

1:1

appliesTo LifeStageProperty1:1

AbundanceCount

itemMeasured NumberValue

hasCount

1:11:1

semType(P2)

⊑

AccuracyQualifier

hasProperty

1:1

hasValue1:1


Example Semantic TypesSemantic types for P2 and P3

S1


S1


S2


S2


P1P2

P4

P3 P5

Observation

semType(P3)

MeasContext

hasContext

1:1

appliesTo LifeStageProperty1:1

AbundanceCount

itemMeasured NumberValue

hasCount

1:11:1

semType(P2)

⊑

AccuracyQualifier

hasProperty

1:1

hasValue1:1

semType(P3) subClassOf Observation and exists hasContext/(MeasurementContext and exists appliesTo/LifeStageProperty and atMost 1/appliesTo) and exists itemMeasured/AbundanceCount and atMost 1/itemMeasured.

semType(P2) subClassOf Observation and exists hasContext/(MeasurementContext and exists appliesTo/LifeStageProperty and atMost 1/appliesTo) and exists itemMeasured/AbundanceCount and atMost 1/itemMeasured and exists hasProperty/AccuracyQualifier and atMost 1/hasProperty.

semType(P3) subClassOf Observation and exists hasContext/(MeasurementContext and exists appliesTo/LifeStageProperty and atMost 1/appliesTo) and exists itemMeasured/AbundanceCount and atMost 1/itemMeasured.

semType(P2) subClassOf Observation and exists hasContext/(MeasurementContext and exists appliesTo/LifeStageProperty and atMost 1/appliesTo) and exists itemMeasured/AbundanceCount and atMost 1/itemMeasured and exists hasProperty/AccuracyQualifier and atMost 1/hasProperty.


Outline• The SEEK Project• Scientific Workflows• The Problem: Reusing Structurally



The Ontology-Driven FrameworkDefine semantic registration mappings

(“semantic views”) to connect structural and semantic types

Use registration mappings to (semi-) automate transformation, based on derived structural correspondences

Depending on the ontologies and registration mappings, it may not be possible to find an appropriate …

(since the correspondence is often under-specified)


The Ontology-Driven Framework



Ps Pt

SemanticType Ps

SemanticType Ps

SemanticType Pt

SemanticType Pt

StructuralType Pt

StructuralType Pt

StructuralType Ps

StructuralType Ps

Desired Connection

Compatible ( )⊑

RegistrationMapping (Output)

RegistrationMapping (Input)



Registration Example (simple XPaths)

/population/sample == semType(P2)/population/sample/meas/cnt == semType(P2).itemMeasured/population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount/population/sample/meas/acc == semType(P2).hasProperty/population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue/population/sample/lsp/text() == semType(P2).hasContext.appliesTo



structType(P2)






structType(P2)

Each sample is an instance of the semantic type






structType(P2)

Each sample’s cnt represents the itemMeasured object






structType(P2)

Each sample’s cnt’s value represents the hasCount value ofthe corresponding itemMeasured object



/cohortTable/measurement == semType(P3)/cohortTable/measurement/obs == semType(P3).itemMeasured/cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount/cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo

<cohortTable> <measurement> <phase>Eggs</cnt> <obs>44,000</acc> </measurement>…<cohortTable>

root cohortTable = (measurement)*elem measuremnt = (phase, obs)elem phase = xsd:stringelem obs = xsd:integer

structType(P3)

… similary for P3 .. … .





Ps Pt

SemanticType Ps

SemanticType Ps

SemanticType Pt

SemanticType Pt

StructuralType Pt

StructuralType Pt

StructuralType Ps

StructuralType Ps

Desired Connection

Compatible ( )⊑



CorrespondenceCorrespondence



Correspondence Example/population/sample == semType(P2)/population/sample/meas/cnt == semType(P2).itemMeasured/population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount/population/sample/meas/acc == semType(P2).hasProperty/population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue/population/sample/lsp/text() == semType(P2).hasContext.appliesTo


Source-side semantic registration mapping

Target-side semantic registration mapping

populationsample *meascnt

xsd:double

xsd:stringlsp

xsd:integer

acc

cohortTablemeasurement *obsxsd:integer

phasexsd:string


Correspondence Example/population/sample == semType(P2)/population/sample/meas/cnt == semType(P2).itemMeasured/population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount/population/sample/meas/acc == semType(P2).hasProperty/population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue/population/sample/lsp/text() == semType(P2).hasContext.appliesTo


Source

Target


xsd:double

xsd:stringlsp

xsd:integer

acc


phasexsd:string

We want to “compose”the registrations to obtain

structural correspondences

We want to “compose”the registrations to obtain

structural correspondences


Correspondence Example/population/sample == semType(P2) /population/sample/meas/cnt == semType(P2).itemMeasured/population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount/population/sample/meas/acc == semType(P2).hasProperty/population/sample/meas/acc/text() == semType(P2).hasProperty.hasValue/population/sample/lsp/text() == semType(P2).hasContext.appliesTo


Source

Target


xsd:double

xsd:stringlsp

xsd:integer

acc


phasexsd:string

/population/sample == semType(P2)

/cohortTable/measurement == semType(P3)

These fragments correspond




Source

Target


xsd:double

xsd:stringlsp

xsd:integer

acc


phasexsd:string

/population/sample/meas/cnt == semType(P2).itemMeasured

/cohortTable/measurement/obs == semType(P3).itemMeasured





Source

Target


xsd:double

xsd:stringlsp

xsd:integer

acc


phasexsd:string

/population/sample/meas/cnt/text() == semType(P2).itemMeasured.hasCount

/cohortTable/measurement/obs/text() == semType(P3).itemMeasured.hasCount





Source

Target


xsd:double

xsd:stringlsp

xsd:integer

acc


phasexsd:string

/population/sample/lsp/text() == semType(P2).hasContext.appliesTo

/cohortTable/measurement/phase/text() == semType(P3).hasContext.appliesTo






Ps Pt

SemanticType Ps

SemanticType Ps

SemanticType Pt

SemanticType Pt

StructuralType Pt

StructuralType Pt

StructuralType Ps

StructuralType Ps

Desired Connection

Compatible ( )⊑



CorrespondenceCorrespondence

Generate (Ps)(Ps)


Transformation


Example Result (XQuery)Based on the structural correspondences

and certain assumptions, we derive the transformation XQuery:

<cohortTable> { for $s in /population/sample return <measurement> { for $c in $s/meas/cnt return <obs>{$c/text()}</obs> } { for $l in $s/lsp return <phase>{$l/text()}</phase> } </measurement> }</cohortTable>


Assumptions Made(or why this may not work for you…)

• Common XPath prefixes refer to the same element

• Elements in correspondences have compatible cardinalities– source is equivalent or stricter than

target (e.g., + is stricter than *)

• Primitive data types are compatible

introduction to databases: from data to knowledge bases instructors: bertram ludaescher kai lin...

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