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
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 (
). A final experiment demonstrates the validity of the approach through absolute quality indices for clustering results.