In order to overcome the limitations of deductive logic-based approaches to deriving operational knowledge from ontologies, especially when data come from distributed sources, inductive (instance-based) methods may be better suited, since they are usually efficient and noise-tolerant. In this paper we propose an inductive method for improving the instance retrieval and enriching the ontology population. By casting retrieval as a classification problem with the goal of assessing the individual class-memberships w.r.t. the query concepts, we propose an extension of the k-Nearest Neighbor algorithm for OWL ontologies based on an entropic distance measure. The procedure can classify the individuals w.r.t. the known concepts but it can also be used to retrieve individuals belonging to query concepts. Experimentally we show that the behavior of the classifier is comparable with the one of a standard reasoner. Moreover we show that new knowledge (not logically derivable) is induced. It can be suggested to the knowledge engineer for validation, during the ontology population task.
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