Affiliations: Dipartimento di Informatica, Università degli
studi di Bari, Campus Universitario, Via Orabona 4, 70125 Bari, Italy. E-mail:
{esposito,claudia.damato,fanizzi}@di.uniba.it
Abstract: This work focusses on the problem of clustering resources contained
in knowledge bases represented throughmulti-relational standard languages that
are typical for the context of the Semantic Web, and ultimately founded in
Description Logics. The proposed solution relies on effective and
language-independent dissimilarity measures that are based on a finite number
of dimensions corresponding to a committee of discriminating features, that
stands for a context, represented by concept descriptions in Description
Logics. The proposed clustering algorithm expresses the possible clusterings in
tuples of central elements: in this categorical setting, we resort to the
notion of medoid, w.r.t. the given metric. These centers are iteratively
adjusted following the rationale of fuzzy clustering approach, i.e. one where
the membership to each cluster is not deterministic but graded, ranging in the
unit interval. This better copes with the inherent uncertainty of the knowledge
bases expressed in Description Logics which adopt an open-world semantics. An
extensive experimentation with a number of ontologies proves the feasibility of
our method and its effectiveness in terms of major clustering validity indices.
Keywords: Clustering, Multi-relational learning, Description Logics, Ontology, Semantic Web Mining
