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. Key words: Description Logics, Hierarchical Clustering Algorithm, Medoids, Semantic Web

• Type: Conference
• Year: 2007
• Where: KES
• Authors: Nicola Fanizzi, Claudia d'Amato
• Keywords: Hierarchical Clustering Algorithm, KES 2007, Relational Knowledge Bases, Semantic Web