A hierarchical clustering method for semantic knowledge bases

Abstract

This work presents a clustering method which can be applied to relational knowledge bases. Namely, it can be used to discover interesting groupings of semantically annotated resources in a wide range of concept languages. The method exploits a novel dissimilarity measure that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features, represented by a group of concept descriptions (discriminating features). The algorithm is an adaptation of the classic BISECTING K-MEANS to complex representations typical of the ontology in the Semantic Web. We discuss its complexity and the potential applications to a variety of important tasks. © Springer-Verlag Berlin Heidelberg 2007.