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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Partitional Conceptual Clustering of Web Resources Annotated with Ontology Languages
verfasst von : Floriana Esposito, Nicola Fanizzi, Claudia d’Amato
Erschienen in: Knowledge Discovery Enhanced with Semantic and Social Information
Verlag: Springer Berlin Heidelberg
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The paper deals with the problem of cluster discovery in the context of Semantic Web knowledge bases. A partitional clustering algorithm is presented. It is applied for grouping resources contained in knowledge bases and expressed in the standard ontology languages. The method exploits a language-independent semi-distance measure for individuals that is based on the semantics of the resources w.r.t. a context represented by a set of concept descriptions (discriminating features). The clustering algorithm adapts
Bisecting k-Means
method to work with medoids. Besides, we propose simple mechanisms to assign each cluster an intensional definition that may suggest new concepts for the knowledge base (
vivification
). A final experiment demonstrates the validity of the approach through absolute quality indices for clustering results.