A hierarchical clustering method for semantic knowledge bases

Authors:
Nicola Fanizzi;Claudia d'Amato
Affiliations:
Dipartimento di Informatica, Università degli Studi di Bari, Campus Universitario, Bari, Italy;Dipartimento di Informatica, Università degli Studi di Bari, Campus Universitario, Bari, Italy
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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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.