Methodologies, Tools and Languages. Where is the Meeting Point?

Methodologies, Tools and Languages.Where is the meeting point? Asunción Gómez-Pérez

Asunción Gómez-Pérez

Mariano Fernández-López Oscar Corcho

Methodologies, Tools and Languages.Where is the Meeting Point?

Artificial Intelligence LaboratoryTechnical University of Madrid (UPM)

Spain


Index

•Methodologies

•Technology

•Languages


1. Presenting the most representative methodologies

- Uschold & King- Grüninger & Fox- Bernaras et alia- Methontology- Sensus

2. Performing an analysis of such methodologies against the same framework of reference

3. Out of this presentation: ontology learning methods

Methodologies


Uschold’s Methodology

1. Identify Purpose and Scope

2. Building the ontology

•Ontology Capture

•Ontology Coding

• Integrating existing ontologies

3. Evaluation

4. Documentation

5. Guideliness for each phase

Uschold, M.; Grüninger, M. ONTOLOGIES: Principles, Methods and Applications.Knowledge Engineering Review. Vol. 11; N. 2; June 1996.

•Identify key concepts and relationships•Produce unambiguous text definitions•Identify terms to refer to such concepts and relations

•Commit to a meta-ontology•Choose a representation language•Write the code

How and whether to reuse ontologiesthat already exist


TOVE Methodology

Identify intuitively possible applications and solutions

Identify Queries:•Answers: Axioms

Formal definitions•Questions: Terminology

Conditions under whichthe solutions to the questions

are complete

MotivatingScenarios

InformalCompetency

Questions

FormalTerminology

FormalCQ

FormalAxioms

CompletenessTheorems

As an entailment of consistency problems

with respect tothe axioms in the ontology

Defined as a first-order sentenceusing the predicates of the ontology

Objects

AttributesRelations

Constants Variables

FunctionsPredicates

KIFUschold, M.; Grüninger, M. ONTOLOGIES: Principles, Methods and Applications.Knowledge Engineering Review. Vol. 11; N. 2; June 1996.


Methodology used on the KACTUS project

Specification of the application

Preliminary design based on relevant

top-level ontological categories

Domain Ontology

Redefine

A botton-up approach for building ontologiesBuild a preliminary ontology for refinement and augment with new definitions

A. Bernaras; I. Laresgoiti; J. Corera. Building and reusing ontologies for electrical network applicationsECAI96. 12th European Conference on Artificial Intelligence. 1996. 298-302


SENSUS as a basis for a domain-specific ontology

Top-down approach

Linking Domain Specific Terms to a broad Coverage Ontology

To identify the terms in SENSUS that are relevant to a particular domain andthen prune the skeletal ontology using heuristics

B. Swartout; R. Patil; k. Knight; T. Russ. Toward Distributed Use of Large-Scale OntologiesOntological Engineering. AAAI-97 Spring Symposium Series. 1997. 138-148.

SENSUS SENSUS

Skeletal Ontology


METHONTOLOGY Framework

The world of ontologies

• To set up a life cycle

• Development process

Tools

METHODOLOGYItem 1: It is necessary…

… … .Item 2: Since …

Define-Ontology

(Imported ontologies ....)

ONTOLOGYCan be public

Gómez-Pérez, A. Knowledge Sharing and Reuse. In the Handbook of Applied Expert Systems. CRC Press. 1998.


Ontology Life Cycle

Specification Conceptualization Maintenance

Technical activities

Support activities

Acquisition

Evaluation

Documentation

Configuration Management

Formalization

Integration

Implementation

Management activities

Planification Control

Quality control

Fernández-López, M.; Gómez-Pérez, A.; Rojas M.D.Ontology’s Crossed Life Cycle. Lectures Notes in Artificial Intelligence Nº 1937. October 2000


Inter-dependenciesInter-dependencies refer the relationship between activities carried out when building different ontologies

O1

O3

O2

Fernández-López, M.; Gómez-Pérez, A.; Rojas M.D.Ontology’s Crossed Life Cycle.Lectures Notes in Artificial Intelligence Nº 1937. October 2000


Monatomic Ions Ontology: Life Cycle

Knowledge Acquisition

Specification

Find and Locate Ontologies

Inspect Content and Granularity

Selection of Standard-Units and Chemical-Elements

•No taxonomic organization

•Several versions

Preliminary Evaluation of Standard Units at Ontolingua S.

Preliminary Evaluation of Chemicals at ODE



Integration

Specification


Conceptualization

Reuse of Chemical and Standard Units

Standard UnitsOntological Reengineering

Merge

Revision ofChemical Elements

Evaluation

C. Management



Integration

Specification


Conceptualization

Reuse of Chemical and Standard Units


Evaluation Redesing

C. ManagementEvaluation

Reverse Engineering

Implementation

Merge


Evaluation

C. Management



Integration

SpecificationKnowledge Acquisition

Conceptualization


Evaluation Redesing

C. ManagementEvaluation

Reverse Engineering

Implementation

Merge


Evaluation

C. Management

DocumentationEvaluation

Implementation


Ontology’s crossed life cycles

Version 2

Version 3

Version 1CHEMICAL-ELEMENTS

Development

ODE

Development

STANDARD UNITS

Ontolingua Server

Evaluation of v.1

Evaluation of v.2

Evaluation of v.3

Merge + Evaluation + Configurationmanagement

Reengineering + Configuration management

ODE

Maintenance of Stanford versionEvaluation

CONCEPTUALIZATION

•Conceptualization•Acquisition•Evaluation•Documentation•Integration

SPECIFICATIONPhases

Intra-dependencies

•Specification•Acquisition•Evaluation•Documentation

MONATOMIC IONS

ODE

IMPLEMENTATION

•Implementation•Acquisition•Evaluation•Documentation•Integration

Maintenance


Uschold& King

Grüninger& Fox

Bernaraset alia

Methontol.

Sensus

Man

agem

ent

proc

esse

s Development-oriented processes

Pre-de

velop

.Development

Requi-rements Design

Imple-mentation Post

-devel

op.

Inte

gral

proc

esse

s

Methodology compliance withMethodology compliance with IEEE Standard.IEEE Standard.


Recomen-ded

life cycle

Compliancewith

IEEE Std.

Recommen-ded

techniques

Ontologiesand

applications

Uschold& KingGrüninger& Fox

Bernaraset alia

Methontol.

Sensus

Detail ofthe

methodology

1 domain

1 domain

1 domain

SeveraldomainsSeveraldomains

Very little

Little

Very little

A lot

Medium

Not known

Not known

Not known

Not known

Maturity of the Methodologies


1. None of the methodologies are fully mature if we compare them with theIEEE Standard

- METHONTOLOGY- Grüninger & Fox- Uschold & King- SENSUS- Bernaras et alia

2. The proposals are not unified

3. Methodologies should be supported by tools.

Maturitydegree

Conclusions about methodologies


Index

•Methodologies

•Technology

•Languages


Technological dimension.

• Several ontology editors

– Ontolingua (KSL, Stanford)

– Ontosaurus (ISI)

– OILed (University of Manchester)

– OntoEdit (Ontoprice, Karlsrhue Univ)

– Protégé2000 (SMI, Stanford)

– WebOnto (KMI, Open University)

– WebODE (UPM)

• Some ontology-based services :

–Merge: Chiamera, OntoMorph, ProtégéPromp

–Translation: Protégé2000, WebODE, .....

–Machine learning (knowledge acquisition)


Main Dimensions of this comparative study

– General description features (developers, releases, current uses, etc.)

– Sw architecture and tool evolution

– Interoperability (with other tools & information systems, translations, etc.)

– Underlying knowledge model

– Libraries

– Inference services

– Methodology support

– Cooperation

– Usability aspects: Help system, edition & visualization


General description of tools

EU tools USA tools


Architecture and Evolution

– We are moving into a new generation of Java based Ontology Tools

– WebOnto, OILed, OntoEdit, WebODE, Protégé2000

– Just few tools using databases for storing ontologies:

– Protégé2000, WebODE and OntoEdit (comercial)

– Only backup management in WebODE

– Extensibility facilities in OntoEdit, WebODE and Protégé-2000


Knowledge Model & Inference Engine

Most of the tools have the same expressiveness

•Metaclasses are allowed on Ontosaurus and Protégé-2000

•Most of the tools allow to represent axioms

•Ontolingua and Protégé do not have an inference engine

•OntoEdit, WebODe, Ontosaurus provide evaluation facilities

•Only DL language (OILed and Ontosaurus) allows automatic classifcations

•No exception handling

INFERENCE ENGINE

Knowledge Model


Methodology Support

– Only WebODE gives support to a methodology.

– No project management facilities, no (semi)automatic acquisition facilities, no maintenance and a little suport for verification

– Conceptualization is only included in Ontoedit, Protégé2000 and WebODE

InteroperabilityNew tools export and import to ad-hoc XML and other markup languagesBut..., what is the quality of all these exports?what about the interoperability between tools?

Cooperation

– WebOnto has the most advanced features on collaborative construction

– Tools need more features to ensure a successful collaborative building of ontologies

All help systems must be improved


Technological dimension. Problems• No interoperability between tools

– Different knowledge models

– Different technology

– Difficult integration

• No specialized modules for evaluation, configuration management, automatic

construction of ontologies, upgrades of ontologies, ....

• Translation services between tools

OntoEdit

WebODE

Protégé2000

OILEd

Translation services


Ontology Development Tools

Ontology-Based Applications

Ontology Middleware

Ontology library

Ontologies

Metricsservices

Administrationservices

Ontology selectionservices

Queryservices

Ontology accessservices

...

Ontologyeditor

Ontologymerge

Ontologytranslation

SemanticPortals

KnowledgeManagementBrokers ...

OntologyDevelopment

Suite

Component-basedEasy integrationRAD...

Ontologyacquisition

Ontologybrowser

Ontologymapper

Ontologyevaluation

Ontologyconf. man.

Ontologydocum.

Ontologyevolution

Towards an workbench for ontologies

Methodologies, Tools and Languages.Where is the meeting point? Asunción Gómez-PérezOntology Development Tools

Ontology Middleware

Ontology library

Ontologies

Administrationservices

Ontology selectionservices

Queryservices

Ontology accessservices

...

Ontologyeditor

Ontologymerge

Ontologytranslation

Ontologyacquisition

Ontologybrowser

Ontologymapper

Ontologyevaluation

Ontologyconf. man.

Ontologydocum.

Ontologyevolution

done

In progress

WebODE Workbench Status


Index

•Methodologies

•Technology

•Languages


Motivation (I)

I must develop an ontology.What language do I use to code it???

• The one(s) I like the most?• The one(s) I know the best?• The one(s) supported by an ontology tool?• The one(s) that best fit(s) my needs?

Corcho, O; Gómez-Pérez, A.; A RoadMap on Ontology Specification LanguagesLectures Notes in Artificial Intelligence Nº 1937. October 2000


Motivation (II)

Most decisions are based on the preferences of the developer

But they should be taken on the basis of...- the needs of expressiveness- the needs of reasoning features- ...

of the application which will use the ontology

A deep study of existing languages is needed in order to avoid blind decisions


RDF(S), OIL, XOL, OML/CKML, ...

Ontolingua, OKBC, FLogic, LOOM, OCML, ...

Evaluation Framework (EKAW-00, Corcho and Gomez-Perez)

Prototype of e-commerce ontology

Comparison & Assessment


Evaluation Framework

KR(Expressiveness)

Classes: MetaclassesAttributesFacets

Taxonomies and partitions

Procedures

Relations/FunctionsInstances/Individuals/Facts/Claims

Axioms

Production Rules

Inference mechanisms(Reasoning)

ExceptionsAutomatic Classifications

InheritanceMonotonic, non-monotonicSimple, Multiple

Execution of procedures

Constraint Checking

Reasoning with rules


Ontology Specification Languages (I)

Traditional ontology languages

•Ontolingua/KIF

•OKBC

•OCML

•LOOM

•Flogic

Standards & Recommendations of W3C

Ontology specification languages

•XML

XML

•RDF(S)

RDF(S)

•OIL

OIL

•DAML+OILDAML+OIL

•OML/CKML

OML

•XOL

XOL

•SHOE

SHOE


Language

Ontolingua/KIF

OKBC

OCML

LOOM

FLogic

SHOE

XOL

OIL

DAML+OIL

OML/CKML

RDF(S)

Formalisms and languages

Formalism

FramesFrames

Description LogicDescription Logic

Semantic NetsSemantic Nets

Conceptual GraphsConceptual Graphs

First order LogicFirst order Logic


Comparison & Assessment (II)

• Assessment: Which language should I choose???

– High expressiveness needs Traditional languages

– Automatic Classifications Description logic

– Ontology exchange Web-based languages

– Agent-based architectures

• Ontology exchange Web-based languages• Reasoning Traditional languages


Methodologies

Uschold y King

Grüninger y Fox

Bernaras et alia

SENSUS

Tools

Oiled

OntoEdit

WebODE

Protégé-2000

Ontolingua Server

Ontosaurus

WebOnto

METHONTOLOGY

Languages

KIF

Ontolingua

OKBC

LOOM

OCML

Flogic

CARIN

DAML+OIL

OIL

RDF (S)

RDF

XML

Conclusion Methodologies, tools and languages


Asunción Gómez-Pérez

Mariano Fernández-López Oscar Corcho

Methodologies, Tools and Languages.Where is the Meeting Point?

Artificial Intelligence LaboratoryTechnical University of Madrid (UPM)

Spain

Methodologies, Tools and Languages. Where is the Meeting Point?

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