2010Unpublished venueRequires access

A Framework for Ontology Integration and Evaluation

Yanhui Lv, Chong Xie

Open publisher page 7 citations

Abstract

Ontology, as a representation of shared conceptualization of specific domain, is a key technology for the Semantic Web. In recent years, many ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies we need a flexible mechanism to integrate ontologies. In this paper we present a framework for ontology integration which combines ontology similarity measures and ontology integration algorithm. After integrating, we perform pruning and checking the consistency of the integrated ontology. Further, we evaluate our approach in terms of ontology quality.

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What this paper is about

Ontology, as a representation of shared conceptualization of specific domain, is a key technology for the Semantic Web. In recent years, many ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies we need a flexible mechanism to integrate ontologies. In this paper we present a framework for ontology integration which combines ontology similarity measures and ontology integration algorithm. After integrating, we perform pruning and checking the consistency of the integrated ontology. Further, we evaluate our approach in terms of ontology quality.

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Available abstract

Ontology, as a representation of shared conceptualization of specific domain, is a key technology for the Semantic Web. In recent years, many ontologies have been developed and many of these ontologies contain overlapping information. To be able to use multiple ontologies we need a flexible mechanism to integrate ontologies. In this paper we present a framework for ontology integration which combines ontology similarity measures and ontology integration algorithm. After integrating, we perform pruning and checking the consistency of the integrated ontology. Further, we evaluate our approach in terms of ontology quality.

Key concepts: Ontology-based data integration, Computer science, Ontology, Process ontology, Upper ontology, Ontology alignment, Ontology components, Information retrieval

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