Query Answering and Ontology Population: an Inductive Approach

A paper written by Claudia d'Amato, Nicola Fanizzi and Floriana Esposito. It was presented at the ESWC2008. It is about Inductive Learning, Uncertainty, Description Logic, Similalrity Measure and Ontology population and query unswering

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[edit] Abstract

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 \emph{k-Nearest Neighbor} algorithm for Description Logic KBs. It is based on the exploitation of an \emph{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.

This data has been imported from the ESWC2008 data