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Context Knowledge Base for Ontology Integration
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0003-4348-4239
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.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 14:14
Keyword [en]
ontology integration, semantic and pragmatic integration, context, knowledge base, rules
National Category
Computer Science
Research subject
SRA - ICT
Identifiers
URN: urn:nbn:se:kth:diva-154068ISBN: 978-91-7595-314-4 (print)OAI: oai:DiVA.org:kth-154068DiVA: diva2:755363
Public defence
2014-11-07, Lounge, Electrum 229, KTH/ICT, Kista, 13:00 (English)
Opponent
Supervisors
Note

QC 20141017

Available from: 2014-10-17 Created: 2014-10-13 Last updated: 2014-10-22Bibliographically approved
List of papers
1. Ontology Integration with Non-Violation Check and Context Extraction
Open this publication in new window or tab >>Ontology Integration with Non-Violation Check and Context Extraction
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.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 13:02
Keyword
ontology matching and integration, semantic and pragmatic matching, semantic techniques, context, ontology and rules
National Category
Computer Systems
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-117555 (URN)
Presentation
2013-02-22, Sal E, Forum, Isafjordsgatan 39, Kista, 10:00 (English)
Opponent
Supervisors
Note

QC 20130201

Available from: 2013-02-01 Created: 2013-01-31 Last updated: 2014-10-17Bibliographically approved
2. Conceptual ontology intersection for mapping and alignment of ontologies
Open this publication in new window or tab >>Conceptual ontology intersection for mapping and alignment of ontologies
2013 (English)In: Agent and Multi-Agent Systems in Distributed Systems - Digital Economy and E-Commerce, Springer Berlin/Heidelberg, 2013, 105-124 p.Chapter in book (Refereed)
Abstract [en]

Combining ontologies can enrich knowledge within a domain and support the development and use of advanced services. This requires matching and combining the relevant ontologies for specific services, which can be supported by mapping and alignment of several ontologies. However, these techniques are not enough since the ontologies are often heterogeneous and difficult to combine. To overcome these problems, a conceptual ontology intersection is provided to map and align contents of the ontologies. This intersection is a conceptual ontology bridge between ontologies and contains parts from the involved ontologies. The contents are extracted by syntactic mapping and synonym alignment using an ontology repository, a rule base and a synonym lexicon using agents. The result is a set of concepts that together constitute the intersection, which is used for combining new incoming ontologies and, thereby, providing complex services.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013
Series
Studies in Computational Intelligence, ISSN 1860-949X ; 462
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-143286 (URN)2-s2.0-84893154439 (Scopus ID)
Note

QC 20140320

Available from: 2014-03-20 Created: 2014-03-19 Last updated: 2017-11-29Bibliographically approved
3. Ontology Integration with Contextual Information
Open this publication in new window or tab >>Ontology Integration with Contextual Information
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

By studying ontologies in an ontology repository, context rules are developed to improve ontology integration result. A context rule contains conditions for identifying a context. These context conditions are described by, so called, context criteria, which are, e.g., author and domain of an ontology. When the conditions, in a rule, are met, the rule is fired and the contextual information, in the body of the rule, is inserted into the reasoner, which is used for ontology integration. An example shows the construction of a context rule. The rule is used for an ontology integration. The integration result is indeed improved comparing with integration without contextual information.

Place, publisher, year, edition, pages
Scitepress online digital library, 2014
Series
Scitepress online digital library
Keyword
ontology integration and merging, context, rules
National Category
Computer Systems
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-154139 (URN)2-s2.0-84902344588 (Scopus ID)978-989758015-4 (ISBN)
Conference
6th international conference on Agent and Artificial Intelligence, ICAART 2014, Angers, France, 6-8 March, 2014
Note

QC 20150324

Available from: 2014-10-14 Created: 2014-10-14 Last updated: 2015-03-24Bibliographically approved
4. A method of identifying ontology domain
Open this publication in new window or tab >>A method of identifying ontology domain
2014 (English)In: Procedia Computer Science 35, ELSEVIER, 2014, Amsterdam: Elsevier B.V. , 2014, 505-513 p.Conference paper, Published paper (Refereed)
Abstract [en]

Metadata, such as the domain description and the purpose of an ontology, can be used to describe the context of ontologies for ontology integration. However, these metadata are not always available in ontologies. To solve the problem, a method is developed to automatically discern the domain of an ontology. This method uses a so-called core domain ontology, rulesand an ontology reasoner to identify the domain. The core domain ontology is a light weight ontology that consists of the essential concepts of a domain. Rules and the ontology reasoner are used to test if the core domain ontology is consistent with an ontology for which the domain needs to be identified. If the two ontologies are not in violation, then the method confirms the consistency between them, that is, the test ontology shares the same domain as the core domain ontology. If the core domain ontology shows inconsistency with the test ontology, they do not share the same domain and then theontology can be used to compare with another core domain ontology. Experiments on the core domain ontology for the conference domain show good results. Ten ontologies of mixed domains are compared with the core conference ontology. Eight ontologies’ domain are correctly identified, out of which, four ontologies are identified as sharing the same ontology domain.

Place, publisher, year, edition, pages
Amsterdam: Elsevier B.V., 2014
Series
Procedia Computer Science , available online at www.sciencedirect.com, 35
Keyword
domain identification of ontology, metadata, semantic ontology integration
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-154087 (URN)10.1016/j.procs.2014.08.131 (DOI)000345394100051 ()2-s2.0-84924120104 (Scopus ID)
Conference
18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Gdynia, Poland, 15-17 September, 2014
Note

QC 20150121

Available from: 2014-10-14 Created: 2014-10-14 Last updated: 2015-01-21Bibliographically approved
5. A method for creating and using a context knowledge base for ontology integration
Open this publication in new window or tab >>A method for creating and using a context knowledge base for ontology integration
2014 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291Article in journal, Editorial material (Refereed) Submitted
Abstract [en]

Ontology integrations are applied for generating new ontologies used for providing advanced services. Although many algorithms and systems for ontology integration have been proposed, it is still very difficult to achieve semantic and pragmatic ontology matching and integration. To tackle the problems, a method of building and using a context knowledge base is needed to get the context of ontologies used for matching and integration. In the method, a context knowledge base with context rules is developed upon an ontology repository to improve the ontology integration. In the context knowledge base, context rules use Ontology Metadata Vocabulary to describe contexts. The stored ontologies that in the repository satisfy a particular context, are extracted and integrated automatically to form contextual information. In the contextual information, both the consistent ontological definitions and the different perspectives from various ontologies of string-identical entities are used. For new ontology integrations, context rules are searched and triggered according to the context of the ontology integration at hand. The contextual information of the rules is aggregated for improving the new ontology integration. Meta-rules are also automatically built to be able to apply the context rules in a hierarchical relation. Experiments of integrations show that the context knowledge base provides extra contextual information for ontology integration. Moreover, when comparing an integration using the context knowledge base to an ontology integration without the context knowledge base, the results of the contextual ontology integrations are improved. At the same time, it is observed that the quantity and the quality of the stored ontologies, in a repository, determine the quality of the context knowledge base. The better quality and quantity of the context knowledge base, the higher degree of improvement it infers to the contextual ontology integrations.

Place, publisher, year, edition, pages
Elsevier, 2014
Keyword
ontology integration, context, knowledge base, rules, contextual ontology intersection
National Category
Computer Systems
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-154140 (URN)
Note

QS 2015

Available from: 2014-10-14 Created: 2014-10-14 Last updated: 2017-12-05Bibliographically approved

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  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
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  • nn-NO
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  • Other locale
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Output format
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