A clustering method is presented which can be applied to semanti-cally annotated resources in the context of ontological knowledge bases. This method can be used to discover emerging groupings of resources expressed in the standard ontology languages. The method exploits a language-independent semi-distance measure over the space of resources, that is based on their semantics w.r.t. a number of dimensions corresponding to a committee of discriminating features represented by concept descriptions. A maximally discriminating group of features can be constructed through a feature construction method based on genetic programming. The evolutionary clustering algorithm proposed is based on the notion of medoids applied to relational representations. It is able to induce a set of clusters by means of a fitness function based on a discernibility criterion. An experimentation with some ontologies proves the feasibility of our method.
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