2009 | OriginalPaper | Chapter
Partitional Conceptual Clustering of Web Resources Annotated with Ontology Languages
Authors: Floriana Esposito, Nicola Fanizzi, Claudia d’Amato
Publisher: Springer Berlin Heidelberg
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.
