Approximate Classification of Semantically Annotated Web Resources Exploiting Pseudo-metrics Induced by Local Models

We present a classification method, founded in the instance-based learning and the disjunctive version space approach, for performing approximate retrieval from knowledge bases expressed in Description Logics. The method supplies answers even if the knowledge base of reference is inconsistent or incomplete. Moreover, the method may also induce new knowledge that can be suggested to the knowledge engineer, thus making the ontology population task semi-automatic.