Evolutionary Conceptual Clustering of Semantically Annotated Resources

A clustering method is presented which can be applied to knowledge bases storing semantically annotated resources. The method can be used to discover groupings of structured objects expressed in the standard concept languages employed in the Semantic Web. The method exploits effective language-independent semi-distance measures over the space of resources. These are based on their semantics w.r.t. a number of dimensions corresponding to a committee of features represented by a group of discriminating concept descriptions. We show how to obtain a maximally discriminating group of features through a feature construction procedure based on genetic programming. The evolutionary clustering algorithm employed is based on the notion of medoids applied to relational representations. It is able to induce an optimal set of clusters by means of a proper fitness function based on the defined distance and the discernibility criterion. An experimentation with some real ontologies proves the feasibility of our method.

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