A Hierarchical Clustering Method for Semantic Knowledge Bases

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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
Nicola Fanizzi, Claudia d'Amato
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where KES
Authors Nicola Fanizzi, Claudia d'Amato
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