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Ontology Integration with Non-Violation Check and Context Extraction
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2013 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

Matching and integrating ontologies has been a desirable technique in areas such as data fusion, knowledge integration, the Semantic Web and the development of advanced services in distributed system. Unfortunately, the heterogeneities of ontologies cause big obstacles in the development of this technique.

This licentiate thesis describes an approach to tackle the problem of ontology integration using description logics and production rules, both on a syntactic level and on a semantic level. Concepts in ontologies are matched and integrated to generate ontology intersections. Context is extracted and rules for handling heterogeneous ontology reasoning with contexts are developed.

Ontologies are integrated by two processes. The first integration is to generate an ontology intersection from two OWL ontologies. The result is an ontology intersection, which is an independent ontology containing non-contradictory assertions based on the original ontologies. The second integration is carried out by rules that extract context, such as ontology content and ontology description data, e.g. time and ontology creator. The integration is designed for conceptual ontology integration. The information of instances isn't considered, neither in the integrating process nor in the integrating results.

An ontology reasoner is used in the integration process for non-violation check of two OWL ontologies and a rule engine for handling conflicts according to production rules. The ontology reasoner checks the satisfiability of concepts with the help of anchors, i.e. synonyms and string-identical entities; production rules are applied to integrate ontologies, with the constraint that the original ontologies should not be violated.

The second integration process is carried out with production rules with context data of the ontologies. Ontology reasoning, in a repository, is conducted within the boundary of each ontology. Nonetheless, with context rules, reasoning is carried out across ontologies. The contents of an ontology provide context for its defined entities and are extracted to provide context with the help of an ontology reasoner. Metadata of ontologies are criteria that are useful for describing ontologies. Rules using context, also called context rules, are developed and in-built in the repository. New rules can also be added.

The scientific contribution of the thesis is the suggested approach applying semantic based techniques to provide a complementary method for ontology matching and integrating semantically. With the illustration of the ontology integration process and the context rules and a few manually integrated ontology results, the approach shows the potential to help to develop advanced knowledge-based services.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. , 78 p.
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 13:02
Keyword [en]
ontology matching and integration, semantic and pragmatic matching, semantic techniques, context, ontology and rules
National Category
Computer Systems
Research subject
URN: urn:nbn:se:kth:diva-117555OAI: diva2:602098
2013-02-22, Sal E, Forum, Isafjordsgatan 39, Kista, 10:00 (English)

QC 20130201

Available from: 2013-02-01 Created: 2013-01-31 Last updated: 2014-10-17Bibliographically approved
In thesis
1. Context Knowledge Base for Ontology Integration
Open this publication in new window or tab >>Context Knowledge Base for Ontology Integration
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Ontology integration is a process of matching and merging two ontologies for reasons such as for generating a new ontology, thus creating digital services and products. Current techniques for ontology integration, used for information and knowledge integration, are not powerful enough to handle the semantic and pragmatic heterogeneities. Because of the heterogeneities, the ontology matching and integration have shown to be a complex problem, especially when the intention is to make the process automatic.

This thesis addresses the problem of integrating heterogeneous ontologies, first, by exploring the context of ontology integration, secondly, by building a context knowledge base, and thirdly, by applying the context knowledge base. More specifically, the thesis contributes a context knowledge base method for ontology integration, CKB-OI method, which contains:

1) A method of building a context knowledge base by extracting context and contextual information from ontologies in an ontology repository to improve ontology integration.

2) A method of refining the result of ontology integration with the help of the context knowledge base and expanding the context rules in the context knowledge base.

In the first method, the context of the ontology integration is identified by examining the content and metadata of the integrated ontologies. The context of an ontology integration contains the information describing the integration, such as the domain of ontology, the purpose of ontology, and the ontology elements involved. Context criteria, such as the metadata of ontologies and the element of ontologies in the repository, are used to model the context. The contextual information is extracted and integrated from ontologies in an ontology repository, using an ontology integration process with non-violation check. With the context and the contextual information, a context knowledge base is built. Since this is built by reusing ontologies to provide extra information for new ontology integration in the same context, it is quite possible that the context knowledge base will improve the earlier ontology integration result.

A method for identifying the domain of an ontology is also proposed to help in building and using the context knowledge base. Since the method considers the semantic and pragmatic heterogeneities of ontologies, and uses a light-weight ontology representing a domain, this work increases the semantic value of the context knowledge base.

In the second method, the context knowledge base is applied to the result of an ontology integration process with a non-violation check, which in turn results in an ontology intersection. The contextual information is searched for and extracted from the context knowledge base and then applied on the ontology intersection to improve the integration result. The ontology non-violation check integration process is adjusted and adopted in the method. Moreover, the context knowledge base is expanded with perspective rules, with which the different views of ontologies in a context are preserved, and reused in future ontology integration.

The results of the CKB-OI methods are: 1) a context knowledge base with rules that consider semantic and pragmatic knowledge for ontology integration; 2) contextual ontology intersection (COI) with the refining result compared to the ontology intersection (OI), and 3) an extended context knowledge base with the different views of both ontologies. For evaluation, ontologies from the Ontology Alignment Evaluation Initiative (OAEI) and from ontology search engines Swoogle and Watson have been used for testing the proposed methods. The results show that the context knowledge base can be used for improving heterogeneous ontologies integration, hence, the context knowledge base provides semantic and pragmatic knowledge to integrate ontologies. Also, the results demonstrate that ontology integration, refined with the context knowledge base, contains more knowledge without contradicting the ontologies involved in our examples.


Abstract [sv]

Ontologi-integration är en process för att matcha och sammanfoga två ontologier för att t.ex. generera en ny ontologi, och därmed skapa digitala tjänster och produkter. Aktuella tekniker för ontologi- integration, som används för information och kunskapsintegration, är inte tillräckligt kraftfulla för att hantera semantiska och pragmatiska heterogeniteter. På grund av heterogeniteter, har ontologi- matchning och -integration visat sig utgöra ett komplext problem, särskilt när avsikten är att göra processen automatisk.

Denna avhandling behandlar problemet med att integrera heterogena ontologier; för det första genom att undersöka kontexten för ontologi-integrationen, för det andra genom att bygga en kunskapsbas för kontexten, och för det tredje genom att tillämpa denna kunskapsbas. Mer specifikt bidrar avhandlingen med CKB-OI-metoden för ontologi-integration, vilken innehåller:

1)      En metod för att bygga en kontextkunskapsbas, genom att extrahera sammanhang och kontextuell information från ontologier i ett ontologi-förvar för att förbättra ontologi-integrationen.

2)      En metod för att förfina resultatet av ontologi-integration med hjälp av kontextkunskapsbasen och för att utöka kontextreglerna i kunskapsbasen.

I metod nr. 1 identifieras kontexten genom att undersöka innehållet och metadata för de ontologier, som ska integrereras. Kontexten innehåller information som beskriver integrationen, till exempel domän och syfte för varje ontologi, samt element som ingår i respektive ontologi. Kontexten  modelleras med kriterier, såsom metadata och element för ontologierna i förvaret. Den kontextuella informationen extraheras och integreras med användning av en integrationsprocess med icke-överträdelsekontroll. Kontextkunskapsbasen byggs utav kontext samt kontextuell information. Eftersom kunskapsbasen är byggd av återanvända ontologier för att ge ytterligare information till ontologi-integrationen inom samma kontext, så är det mycket möjligt att kontextkunskapsbasen kommer att förbättra det tidigare integrationsresultatet.

En metod för att identifiera domänen för en ontologi föreslås också, för att hjälpa till att bygga och använda kontextkunskapsbasen. Eftersom metoden tar hänsyn till de semantiska och pragmatiska heterogeniteterna hos ontologier, och använder en enkel ontologi för att representera en domän, så ökar detta arbete det semantiska värdet av kontextkunskapsbasen.

I metod nr. 2 tillämpas kontextkunskapsbasen på resultatet av en ontologi-integrationsprocess med icke-överträdelsekontroll, vilket i sin tur resulterar i ett ontologisnitt. Den kontextuella informationen extraheras från kontextkunskapsbasen och appliceras sedan på ontologisnittet för att förbättra integrationsresultatet. Icke-överträdelsekontrollen i integrationsprocessen justeras och används på nytt. Dessutom utökas kontextkunskapsbasen med perspektivregler, med vilka de olika vyerna av ontologier i en gemensam kontext bevaras och återanvänds i framtida ontologi-integrationer.

Resultaten av CKB-OI metoden är: 1) en kontextkunskapsbas med regler som avser semantiska och pragmatiska kunskaper om en ontologi-integration; 2) ett kontextuellt ontologisnitt (COI) med ett förfinat resultat jämfört med ontologisnittet (OI) och 3) en utökad kontextkunskapsbas med olika vyer av båda ontologier. För utvärderingen har ontologier från Ontology Alignment Evaluation Initiative (OAEI) samt ontologisökmotorerna Swoogle och Watson använts för att testa de föreslagna metoderna. Resultaten visar att kontextkunskapsbasen kan användas för förbättring av heterogena ontologi-integrationer. Följaktligen tillhandahåller kontextkunskapsbasen semantiska och pragmatiska kunskaper för att integrera ontologier. Dessutom visar resultaten att ontologi-integrationer, utökade med kontextkunskapsbaser, innehåller mer kunskap, utan att motsäga de ontologier som ingår i våra exempel.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2014. 75 p.
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 14:14
ontology integration, semantic and pragmatic integration, context, knowledge base, rules
National Category
Computer Science
Research subject
urn:nbn:se:kth:diva-154068 (URN)978-91-7595-314-4 (ISBN)
Public defence
2014-11-07, Lounge, Electrum 229, KTH/ICT, Kista, 13:00 (English)

QC 20141017

Available from: 2014-10-17 Created: 2014-10-13 Last updated: 2014-10-22Bibliographically approved

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