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.

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.

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.

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.

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.

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.

There exist a lot of ontologies that together can enrich knowledge within one or several related domains, thereby supporting the development of advanced services on the semantic web. This requires matching and integrating ontologies. This paper introduces an ontology matching process that handles the heterogeneities. The result is an intersection of the two original ontologies. An ontology repository stores the original ontologies and the matching results. A rule base is designed to integrate stored ontologies and the matching results with metadata, which is describing the interpretation of these ontologies and ontology matching results. The contribution of our approach is the semantic violation check which results in an ontology intersection that validates in the original ontologies. The metadata is applied with rules to integrate the ontologies so that the ontology and the matching results can be reused.

By integrating ontologies, knowledge represented in these ontologies can be extended and reused for purposes like building new ontologies, composing advanced services on the semantic web and sharing knowledge. The ontology integration is a difficult task because of semantic barriers. In this paper, an approach of building context for ontology integration is presented. The integration of the ontologies is carried out by using context and ontology violation check. To provide context for the integration, context rules are utilized for interpretation and reasoning across ontologies. The ontology violation check is applied in the process of building ontology intersection under the open world assumption. As a result of the integration, an ontology intersection is produced, which is larger than the intersection of entities, but not larger than the union of the original entities.

Data warehouses is a typical example of distributed systems where diverse tools and platforms need to communicate to understand each other. For the communication, metadata integration is significant. Seamless metadata interchange improves the data quality and the system effectiveness. Metadata standards exist, for instance, Common Warehouse MetaModel (CWM), which have enhanced the metadata integration. However, it is far from solving the problem of metadata integration in data warehouse environment. This paper proposes an approach to apply a knowledge-based system that supports the metadata integration. By utilizing the knowledge of software engineers on Common Warehouse MetaModels and the metadata interchange models, the knowledge-based system can give metadata interchange model suggestions. Such a knowledge-based system intends to partly automate the metadata integration to improve the efficiency and the quality of metadata integration in data warehouses.

This paper presents an approach for handling several ontologies in a domain by integrating a knowledge base in a multi-agent system. For some online facilities, like e-business, several ontologies in different languages are needed to match the users' request. Finding the ontologies is one of the tasks, comparing and combining these ontologies is the other. The accomplishment of these tasks depends on the content in the ontologies like tags and structure but, in some cases, also language and matching techniques. Matching the contents is difficult due to differences between ontologies, often resulting from the lack of explicit and exact standards and development guidelines. This complication increases with the ontologies diverged languages. These problems are tackled by applying a multi-agent system wherein the agents, i.e., software agents and meta-agents, use the users' request to search for ontologies and the knowledge base to compare and combine the contents of the ontologies to create an overall solution. The software agents search for ontologies; the meta-agents keep track of the software agents, ontologies and the knowledge base that reasons with the contents of the ontologies.