Inductive Query Answering and Concept Retrieval Exploiting Local Models

We present a classification method, founded in the \emph{instance-based learning} and the \emph{disjunctive version space} approach, for performing approximate retrieval from knowledge bases expressed in Description Logics. It is able to supply answers, even though they are not logically entailed by the knowledge base (e.g.\ because of its incompleteness or when there are inconsistent assertions). Moreover, the method may also induce new knowledge that can be employed to make the ontology population task semi-automatic. The method has been experimentally tested showing that it is sound and effective.