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In the context of Semantic Web, deductive reasoning is used for making explicit the implicit knowledge of a knowledge base KB. Anyway, purely logic-based approaches can fail when data comes from distributed sources, where contradictions usually turn out. Inductive instance-based learning methods can be effectively used in such a case, since they are well known to be efficient and fault tolerant. In this paper we propose an inductive method for improving the concept retrieval and for the performing the ontology population in a semi-automatic way. By casting concept retrieval to a classification problem with the goal of assessing the individual memberships w.r.t. the query concepts, we propose an extension of the k-Nearest Neighbor algorithm for Description Logic KBs. It is based on the exploitation of an entropy-based dissimilarity measure. The procedure retrieves individuals belonging to query concepts, by analogy with other training instances, on the grounds of the classification of the nearest ones w.r.t.\ the dissimilarity measure. We experimentally 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.