Abstract
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 Bisecting k-Means 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 (vivification). A final experiment demonstrates the validity of the approach through absolute quality indices for clustering results.
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Esposito, F., Fanizzi, N., d’Amato, C. (2009). Partitional Conceptual Clustering of Web Resources Annotated with Ontology Languages. In: Berendt, B., et al. Knowledge Discovery Enhanced with Semantic and Social Information. Studies in Computational Intelligence, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01891-6_4
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DOI: https://doi.org/10.1007/978-3-642-01891-6_4
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