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Abstract

In der Informatik sind Ontologien formale Modelle eines Anwendungsbereiches, die die Kommunikation zwischen menschlichen und/oder maschinellen Akteuren unterstützen und damit den Austausch und das Teilen von Wissen in Unternehmen erleichtern. Ontologien zur strukturierten Darstellung von Wissen zu nutzen hat deshalb in den letzten Jahren zunehmende Verbreitung gefunden. Schon heute existieren weltweit tausende Ontologien. Um Interoperabilität zwischen darauf aufbauenden Softwareagenten oder Webservices zu ermöglichen, ist die semantische Integration der Ontologien eine zwingendnotwendige Vorraussetzung. Wie man sieh leicht verdeutlichen kann, ist die rein manuelle Erstellung der Abbildungen ab einer bestimmten Größe. Komplexität und Veränderungsrate der Ontologien nicht mehr ohne weiteres möglich. Automatische oder semiautomatische Technologien müssen den Nutzer darin unterstützen. Das Integrationsproblem beschäftigt Forschung und Industrie schon seit vielen Jahren z. B. im Bereich der Datenbankintegration. Neu ist jedoch die Möglichkeit komplexe semantische Informationen. wie sie in Ontologien vorhanden sind, einzubeziehen. Zur Ontologieintegration wird in diesem Kapitel ein sechsstufiger genereller Prozess basierend auf den semantischen Strukturen eingeführt. Erweiterungen beschäftigen sich mit der Effizienz oder der optimalen Nutzereinbindung in diesen Prozess. Außerdem werden zwei Anwendungen vorgestellt, in denen dieser Prozess erfolgreich umgesetzt wurde. In einem abschließenden Fazit werden neue aktuelle Trends angesprochen. Da die Ansätze prinzipiell auf jedes Schema übertragbar sind, das eine semantische Basis enthält, geht der Einsatzbereich dieser Forschung weit über reine Ontologieanwendungen hinaus.

Pages

S.469-484

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Abstract

In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Heterogeneity problems on the semantic web can be solved, for some of them, by aligning heterogeneous ontologies. This is illustrated through a number of use cases of ontology alignment. Aligning ontologies consists of providing the corresponding entities in these ontologies. This process is precisely defined in deliverable D2.2.1. The current deliverable presents the many techniques currently used for implementing this process. These techniques are classified along the many features that can be found in ontologies (labels, structures, instances, semantics). They resort to many different disciplines such as statistics, machine learning or data analysis. The alignment itself is obtained by combining these techniques towards a particular goal (obtaining an alignment with particular features, optimising some criterion). Several combination techniques are also presented. Finally, these techniques have been experimented in various systems for ontology alignment or schema matching. Several such systems are presented briefly in the last section and characterized by the above techniques they rely on. The conclusion is that many techniques are available for achieving ontology alignment and many systems have been developed based on these techniques. However, few comparisons and few integration is actually provided by these implementations. This deliverable serves as a basis for considering further action along these two lines. It provide a first inventory of what should be evaluated and suggests what evaluation criterion can be used.

Pages

79 S

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Abstract

The focus of research on representing and reasoning with knowledge traditionally has been on single specifications and appropriate inference paradigms to draw conclusions from such data. Accordingly, this is also an essential aspect of ontology research which has received much attention in recent years. But ontologies introduce another new challenge based on the distributed nature of most of their applications, which requires to relate heterogeneous ontological specifications and to integrate information from multiple sources. These problems have of course been recognized, but many current approaches still lack the deep formal backgrounds on which todays reasoning paradigms are already founded. Here we propose category theory as a well-explored and very extensive mathematical foundation for modelling distributed knowledge. A particular prospect is to derive conclusions from the structure of those distributed knowledge bases, as it is for example needed when merging ontologies