ana paula rocha - upeol/tne/apont/ontologies.pdf · ana paula rocha electronic business...

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Ontologies

Ana Paula Rocha

Electronic Business Technologies

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Motivation

Battery

– Different features

– Different prices

– Different utilities

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Ontology

• Origin in philosophy: – Specification of what exists or what we can say about the world

• In AI systems:– what "exists" is what can be represented

• Popular topic since the early ninety

• Several communities of AI research:– Knowledge Engineering/Representation– Natural Language Processing– Intelligent Information– Information Retrieval on the Web

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Ontology

• Reason for the popularity: mainly due to the promise of a shared and common understanding of some domain of knowledge that can be communicated between people and computer

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What is an ontology?

• “An ontology defines the basic terms and relations comprising the vocabulary of a topic area as well as the rules for combining terms and relations to define extensions to the vocabulary” [Neches et al., 1991]

• “An ontology may take a variety of forms, but necessarily it will include a vocabulary of terms, and some specification of their meaning. This includes definitions and an indication of how concepts are inter-related which collectively impose a structure on the domain and constrain the possible interpretations of terms” [Uschold e Jasper, 1999]

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• “is an explicit specification of a conceptualization” [Gruber, 1993]

– Conceptualisation: is a set of definitions that allows one to construct expressions about some physical domain.

– Explicit: means that the concepts and relationships of the abstract model are given explicit terms and definitions.

• “is a formal specification of a shared conceptualization”[Borst, 1997]

– Formal: Refers to the fact that an ontology should be machine-readable.

– Shared: reflects the notion that the ontology captures consensual knowledge, that is, it is not the privilege of some individual, but accepted by a group.

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• The ontology community distinguishes between: ontologies that are mainly a taxonomy; and ontologies that model the domain more deeply providing more constraints on the semantics of the domain

Lightweight:– make scarce or no use of axiomsto model knowledge and clarify the meaning of concepts in the domain. – include concepts, relationships between concepts and properties that describe these concepts.

Heavyweight:– make intensive use of axiomsto model knowledge and restrict domain semantics. – add axioms and restrictions to lightweightontologies

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Ontology

Issues to discuss:

• Ontology construction– methodology

– tools

– languages

• Ontology Learning

• Ontology Mapping

• Ontology Translation and Interoperability

• Applications

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Ontology construction

Language?

Tools?Methodology?

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• Developing an ontology involves (basically):– Define domain and scope

Example– Domain: Wine representation

– Scope: applications that suggest combinations of wines and food

Other scopes:– Helping clients in the restaurant to decide which wine to ask

– Helping buyers of wine

– Helping transactions between wine producer and wine reseller

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• Define classes in the ontology

• Organize classes in a taxonomy (subclass-superclass)

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WineWhite WineRed WineRose Wine

Red Bordeaux

Red Burgundy

Producer

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• Define attributes (slots)

color

alcoholic content

taste

shape

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• Define relations

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Producer

produces {wine}

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• Define instances: elements

• Define axioms: sentences that are always true

• Define functions: example, price calculation

• ….

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Questions about methodology, tools and languages:

– What methodologies are available for building ontologies, or to reuse existing ontologies?

– What is the life cycle of an ontology?

– What tools support the process of developing an ontology?

– What language should be used?

– Which expressivity has a language of ontology?

– The language chosen is appropriate for the exchange of information between different applications?

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Methodologies for Ontology construction

• Enterprise Ontology

• TOVE (Toronto Virtual Enterprise)

• METHONTOLOGY

• On-To-Knowledge

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Enterprise Ontology- Uscholdand King’s Method[Uschold e King, 1995]

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Identify the ontology proposal

Build the ontology: capture, codify and integrate appropriate knowledge from existing ontologies

Evaluate the ontology

Document the ontology

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TOVE (Toronto Virtual Enterprise) [Grüninger e Fox,1995]

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Identify motivationscenarios

Formulate questions to answer

Formal terminology

Formulate questionsin FOL

Specify axioms

Evaluate theontology

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Methontology[Gómez-Pérez, 1998]

– Specify requirements

– Conceptualize the domain of knowledge

– Formalize the conceptual model in a formal language

– Implement a formal model

– Maintain the implemented ontologies

• Activities performed during the construction process : – Knowledge acquisition

– Integration

– Evaluation

– Documentation

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On-To-Knowledge[Staab et al., 2001]

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Refinement

Evaluation

Maintenance

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Conclusions:

• Methontology: – recommended by FIPA (Foundation for Intelligent Physical Agents)

• Proposals not unified: – each group applies its own approach

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Tools for Ontology construction

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Ontolingua WebONTO WebODE

Protégé OntoEdit OilEd

Apollo SymOntoX OntoSaurus

DagEdit DOE IsaViz

SemTalk OntoBuilder DUET

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Protégé[Noy et al., 2000]

http://protege.stanford.edu

– Developed by the Medical Informatics group, Stanford University

Main features:– Open Code

– Standalone application

– Extensible architecture

– Ontology Editor + plugins (library of functionalities)

– Currently imports/exports to Flogic, Jess, OIL, XML, Prolog, OKBC access

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WebODE[Arpírez et al. 2001; Corcho et. al, 2002]

http://delicias.dia.fi.upm.es/webODE

– Developed by the Artificial Intelligence Laboratory, Technical University of Madrid

Main features:– Extensible architecture

– Web application

– Import/export to XML, RDF(S), OIL, DAML+OIL, CARIN, Flogic, Jess, Prolog

– Ontologies stored in relational databases

– Documentation services, evaluation services and merging of ontologies

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• OntoEdit[Sure et al., 2002]

http://ontoserver.aifb.uni-karlshure.de/ontoedit

– Developed by AIFB (Institutf ür Angewandte Informatik und Formale Beschreibungsverfahren), University of Karlsruhe

Main features:– Extensible architecture, based in plugins

– Import/export for Flogic, XML, RDF(S), DAML+OIL

– Two versions: OntoEditFree e OntoEditProfessional

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Tools for Ontology construction: comparison

• Expressiveness:– All the tools allow to represent classes, relations, attributes, instances and

axioms.

• Interoperability– Many of the tools import and export for XML and markup languages.

– There is no study on the quality of translators.

– There is no results on the exchange of ontologies between different tools.

• Methodology– WebODE supports Methontology

– OntoEdit supports On-To-Knowledge

• Cooperative and Collaborative Ontology Construction – WebODE has the more advanced features

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Languages for Ontology construction

Traditional ontology languages

Cycl Ontolingua F-Logic CML OCML Loom KIF

Standard languages for Web

XML RDF

Web-based ontology languages

OIL DAML+OIL SHOE XOL OWL

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Ontology Learning

• Ontology learning, set of methods and techniques used for:– building an ontology from scratch

– enriching, or adapting an existing ontology in a semi-automatic fashion using several sources

• Several approaches exist, using sources like: – texts, instances, databases schemes, XML schemes, ...

• The most widely used and interesting in the Semantic Web context is the approach based on texts

• Ontology Learning from texts:– extract ontologies by applying natural language analysis and machine

learning/linguistic techniques

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Ontology Learning: tools

Tools based on natural language analysis and machine learning techniques:

– Conceptual Clustering, concepts are grouped according to the semantic distance between each other to make up hierarchies.

ASIUM [Faure e Nedellec, 1999], Mo´K [Bisson et al., 2000] e SVETLAN [Chaelandar e Grau, 2000]

– Lexical and Syntatic Analysis

Corporum-Ontobuilder [htt://ontoserver.cognit.no/], LTG [Mikheev e Finch, 1997] e Terminate [Biébow e Szulman, 1999]

– Statistical Approach

Text-To-Onto [Maedche e Staab, 2001]

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Ontology Mapping

• Ontology Mapping can be defined as a function that associates terms and expressions defined in a source ontology with terms and expressions defined in a target ontology

Main tools: – Chimaera, PROMPT, OBSERVER, OntoMorph, Auto-Categorizer,

WebPicker

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Ontology Mapping

• Is a difficult task because:– it requires a thorough verification of inheritance, consistency of

inference, ...

– the relationships can be many-to-one, one-to-many, many-to-many, within a domain or across domains.

• Many tools are limited to:– verify classes or relations

– check consistency

– provide a list of recommendations of what to do

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Translation and Interoperability

• Ontologies are built using different languages– Each language has its syntax, expressiveness and reasoning ability

– based on different paradigms (frames, first-order logic, description logic, etc.)

• Ontologies are built using different development tools– Each tool exports/imports ontologies for one or more languages

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• Translation problem – arises when we decided to reuse an ontology or part of it, with a tool or

language different from that in which the ontology is available.

• Ontology tools should be able to:– exchange ontologies between them

– export/import ontologies in different formats

• If we refer to the exchange of ontologies between different tools, the problem of translation is also known as interoperability between ontology tools

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• An initial proposal was:– use KIF as a format of knowledge exchange in the Ontolingua Server

– which would reduce the number of translators to be developed

Ontolinguaprovides a distributed collaborative environment to browse, create, edit, modify, and use ontologies(http://ksl.stanford.edu/software/ontolingua)

• Proposal failed: – very poor translation quality

– facilities for export but not import, each developer had to build their own translators to Ontolingua and KIF

• New tools for ontology construction– have created their own translators for different languages

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Conclusions

– Problem of translation has not been addressed in an integrated way. Integrated means:

• examine in depth all the problems that appear in translations

• propose theoretical solutions to these problems

• simultaneously provide technological solutions to solve the problems

– No current proposal addresses the problem of the loss of information in translation

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Ontology: applications

• Knowledge Management– integration of heterogeneous, distributed and semi-structured

information resources

• Electronic Commerce– business relationships (buy/sell) between business entities (especially

B2B)

– places such as Yahoo organize your content into categories to help users to navigate

– the United Nations Standard of Products and Services Code<http://www.unspsc.ORG/> contains a taxonomy that organizes products and services to facilitate transactions between B2B sites that agree with the vocabulary defined there (ontological commitment)

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Aplicações de Ontologias

• Intelligent Information – Search engines like Google and AltaVista use ontologies to implement

semantic queries that improve the classic search by keyword.

• Natural Language Processing– Ontologies like WordNet are used to represent grammatical structures

that allow to perform semantic analysis of texts by reducing the ambiguity of natural language semantics. http://www.cogsci.princeton.edu/~wn/

• Enterprise Modelling – Ontologies support the organizational memory of an enterprise, that

allows the interoperation of departments/areas by using a common vocabulary and pre-defined rules. Examples of these ontologies can be found in TOVE and The Enterprise Ontology.

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Conclusions

There is a need to work on creating tools that facilitate:

– Ontology development throughout all the life cycle, including: integration, merging, reengineering, content evaluation, translation into different languages and formats, and content exchange with other tools

– Ontology management: configuration and ontology evolution management

– Ontology support: schedule, documentation, advanced techniques for viewing the contents of the ontology, etc.

– Methodological support for building ontologies

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Some bibliography• [Arpírez et al., 2001] Arpírez JC, Corcho O, Fernández-López M, Gómez-Pérez A. WebODE:

a scalable ontological engineering workbench. In: Gil Y, MusenM, ShavlikJ (eds) First International Conference on Knowledge Capture (KCAP’01). Victoria, Canada. ACM Press (1-58113-380-4), New York, pp 6-13.

• [Biébow e Szulman, 1999] Biébow B, Szulman S. TERMINAE: a linguistic-based tool for the building of a domain ontology. In EKAW’99 –Proceedings of the 11th European Workshop on Knowledge Acquisition, Modellingand management. Dagstuhl, Germany, LCNS, pages 49-66, Berlin, 1999. Springer-Verlag.

• [Bisson et al., 2000] Bisson G, Nedellec C, Cañamero D. Designing Clustering Methods for Ontology Building –The Mo’KWorkbench. In S. Staab, A. Maedche, C. Nedellec, P. WiemerHasting(eds.), Proceedings of the Workshop on Ontology Learning, 14th European Conference on Artificial Intelligence, ECAI’00, Berlin, Germany, August 20-25.

• [Borst, 1997] Borst WN. Construction of Engineering Ontologies. University of Tweenty. Enschede, The Netherlands -Centre for Telematicaand Information Technology.

• [Chaelandar e Grau, 2000] Chaelandar G, Grau B. SVETLAN’-A System to ClassigyWords in Context. In S. Staab, A. Maedche, C. Nedellec, P. Wiemer-Hastings (eds.) Proceedings of the Workshop on Ontology Learning, 14th European Conference on Artificial Intelligence ECAI’00, Berlin, Germany, August 20-25.

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Some bibliography

• [Chalupsky, 2000] Chalupsky H. OntoMorph: a translation system for symbolic knowledge. In: Cohn AG, GiunchigliaF, Selman B (eds) 7th International Conference on Knowledge Representation and Reasoning (KR’00). Breckenridge, Colorado. Morgan Kaufmann Publishers, San Francisco, California, pp 471–482.

• [Corcho e Gómez-Pérez, 2001a] Corcho O, Gómez-Pérez A. WebPicker: Knowledge Extraction from Web Resources. 6th Intl. Workshop on Applications of Natural Language for Information Systems (NLDB'01). Madrid. June, 2001.

• [Faure e Nédellec, 1999] Faure D, Nédellec C. Knowledge acquisition of predicate argument structures from technical texts using machine learning: The system ASIUM. In D. Fenseland R. Studereditors, Proc. Of the 11th European Workshop (EKAW’99), LNAI 1621, pages 329-334. Springer-Verlag.

• [Gómez-Pérez, 1998] Gómez-Pérez A. Knowledge Sharing and Reuse. In: LiebowitzJ (ed) Handbook of Expert Systems. CRC Chapter 10.

• [Gruber, 1993] Gruber TR. A translation approach to portable ontology specification. Knowledge Acquisition 5(2)199–220.

• [Grüninger e Fox, 1995] Grüninger M, Fox MS. Methodology for the design and evaluation of ontologies. In: IJCAI95 Workshop on Basic Ontological Issues in Knowledge Sharing. Montreal, Canada.

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Some bibliography• [McGuinness et al., 2000b] McGuinness DL, Fikes R, Rice J, Wilder S. An environment for

merging and testing large ontologies. In. Proc. 7th Intl. Conf. On Principles of Knowledge Representation and Reasoning (KR2000), Colorado, USA, April 2000.

• [Maedche e Staab, 2001] Maedche A, Staab S. Ontology Learning for the Semantic Web. IEEE Intelligent Systems, Special Issue on the Semantic Web, 16(2).

• [Mikheev e Finch, 1997] Mikheev, A. Finch, S. A Workbench for Finding Structure in Texts. Proceedings of ANLP-97 (Washington D.C.). ACL March 1997. pp 8.

• [Neches et al., 1991] Neches R, Fikes RE, Finin T, Gruber TR, Senator T, Swartout WR. Enabling technology for knowledge sharing. AI Magazine 12(3):36–56.

• [Noy e Musen, 2000] Noy NF, Musen MA. PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In: 17th National Conference on Artificial Intelligence (AAAI’00). Austin, Texas.

• [Staab et al., 2001] Staab S, Studer R, Schnurr HP, Sure Y. Knowledge Processes and Ontologies. IEEE Intelligent Systems, 16(1) (2001).

• [Sure et al., 2002] Sure Y, Erdmann M, Angele J, Staab S, Studer R, Wenke D. OntoEdit: Collaborative Ontology Engineering for the Semantic Web. In: HorrocksI, HendlerJ (eds) First International Semantic Web Conference (ISWC’02). Sardinia, Italy. Springer VerlagLecture Notes in Computer Science (LNCS) 2342. Berlin, Germany, pp 221–235.

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Some bibliography

• [Uschold e Jasper, 1999] Uschold M, Jasper R. A Framework for Understanding and Classifying Ontology Applications. In: BenjaminsVR (ed) IJCAI'99 Workshop on Ontology and Problem Solving Methods: Lessons Learned and Future Trends. Stockholm, Sweden. CEUR Workshop Proceedings 18:11.1–11.12. Amsterdam, The Netherlands (). http://CEUR-WS.org/Vol-18/

• [Uschold e King, 1995] Uschold M, King M. Towards a Methodology for Building Ontologies. In: IJCAI’95 Workshop on Basic Ontological Issues in Knowledge Sharing. Montreal, Canada.

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Ontology Services

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Electronic Institution

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Norms & Rules

Electronic Institution

links to other institutions

legal

financial

VE

Formation

Q-

Negotiation

VE

Operation

Monitoring

VE

Dissolution

Ontology Services

Electronic ContractMAgt EAgtEAgt EAgt

Trust & Reputation

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Ontology

• Common and shared understanding about a domain

• Agents can use ontologies that are not exactly equal to represent their vision of the domain

• Institutional Ontology– defines a business vocabulary to be used by all agents

– includes: Concepts, AgentActions, Predicates

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Institutional Ontology

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Interoperability problem

• In a decentralized and distributed environment, interoperability refers to how the communication takes place between humans and software agents.

• Ontologies are developed by different and heterogeneous people and continue to evolve over time

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Ontology services

In solving the interoperability problem in e-commerce, particularly in B2B transactions, some ontology services are particularly useful:

– Definition of attributes’ dependencies for each product

– Translation of terms between two ontologies for the same domain

– Conversion of values (eg different metrics)

– Report on mandatory or different attributes that are under negotiation

Ontology-based Services Agent

present in the Electronic Institution

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Ontology Services Agent (OSAg)

• The Electronic Institution integrates(among others) anOntology–based Services Agent(OSAg)

• OSAg offers the following services:– Matching terms– Conversion of units

• Matching terms– when an agent does not understand the contents of a message– based on lexical and semantic similarity measures

• comparison of attributes, relationships between concepts, and concepts descriptions

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Matching terms (OSAg)

• Syntactic Similarity between attributes– Calculates a comparison value ”3-gram”

• Syntactic Similarity between descriptions– Only used the most representative words– A "3-grams” matrix is calculated between each word of description– is used the formula rn-grams

nr

n

ii

gramsn

∑=

− = 1max

n

rsim gramsn

attrSetattrSet∑ −=2/1

for each data type: string, integer, float, boolean, has-part

Maxi is the maximum of all comparison results that exist for one attribute type

for all data types

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Matching terms (OSAg)

• Semantic Similarity– Semantic Similarity mesure LCH (Leacock-Chodorow), based on

“WordNet”

• Final Similarity value

– weak correspondence (0.55 – 0.59)

– approximate correspondence (0.6-0.69)

– strong correspondence (0.7 – 1.0)

3

3

12/1

∑=

×= i

imethod

termterm

weightingresultsim

i

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Ontologia

• Ontology Services

– Institutional ontology defines a business vocabulary

– Ontology-based Services Agent solves the interoperability problem

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Relé

Fusível

Vedante Rolamento

Parafuso

comprimento : DoublecabecaParafuso : String = hexagonal, allel, estrelada

Porcas

largura : DoubletipoFreio : String = plático, nylon, mecânico, sem freio Anel

largura : Double

Caixa Direção

percurso : Double Bomba Direção

FarolFrente FarolAtras

Sistema

tipo : String = monotronic, k-jetronic, mono-jetronic

Especif icacaoMotor

descricao

RodasDentadas

numeroDente : IntegertipoDente : String = reto, espiral, cônico

Pneu

largura : DoublerelacaoAspecto : IntegerdiametroInterior : Double

Disco

diametroInterno : Doublelargura : DoublenumFuros : Integermaterial : String = liga de aço, alumínio, aço

Transmissões

comprimento : Doublediametro : DoubletipoSistemaRotula : String = por bolas, por cruzes, de agulhas

ABS

AirBag

SegurançaPassiva

Bomba

pressaoTrabalho : DoubletipoBomba : String = pistões, embulo, ...

DiscoTravão

arqProjeto : arquivo

TuboTravagem


Corrente

largura : Doublecomprimento : DoubletipoDentes : String = quadrado, redondo, trapéziotipoBorracha : String

Cabo da Corrente

comprimento : Doubleresistencia : Integermaterial : String = LISTA

Eletrônica

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Relé-Fusivel

intensidade : IntegersistemaFuncionamento : String = contador, ruptor

Vela

resistencia : Integerdiametro : Doubleassento : String = cônico, anel

Engrenagem

1..n1..n

Transmissão

1..n1..n

1. .n1. .n

1..n1..n

Outros

quantidade : IntegerdiâmetroNominal : String

Farol

potenciaEletrica : IntegernumeroLampada : Integercor : String

Janela

altura : Doublecomprimento : Doubleespessura : DoubletipoCristal : String = laminado simples, laminado duploformato : StringarqProjeto : arquivo

BorrachaVedante

comprimento : DoubletipoBorracha : String = macia, oca, ...

TuboBorracha

diametroInterior : Doublecomprimento : DoublepressaoMaxima : Double

Tinta

cor : Integerkg : Double

SistemaSegurança


1..n1..n

1..n1..n

1..n1..n

SistemaTravagem

1..n1..n

1. .n1. .n

1..n1..n

Vedante-Rolamento

diametroExterior : DoublediametroInterior : DoubleExpessura : DoublenumRotacao : Integer

Motor

11

1..n1..n

1..n1..n

1..n1..n

1..n1..n

Caixa

11

Automovel

conceito

1..n1..n

1..n1..n

1..n1..n

11

1..n1..n

1..n1..n

11

11

11

Componente

quantidade : IntegerpressaoTrabalho : Doublealimentacao : String = mecânica, elétrica

Direção

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11

Sinônimo

descriçãoSinônimo : String

Sinonimizavel

1..n1..n 1..n1..n

temTodas as classes, com excessão das subclasses herdam da classe Sinonimizavel. Nem todos os relacionamentos foram colocados para não dif icultar a visualizaçãoe leitura do diagrama.

UMLspecification for the “car” ontology (example)

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