Computing Probabilistic Least Common Subsumers in Description Logics

Abstract

Computing least common subsumers in description logics is an important reasoning service useful for a number of applications. As shown in the literature, it can, for instance, be used for similarity-based information retrieval where information retrieval is performed on the basis of the similarities of user-specified examples. In this article, we first show that, for crisp DLs, in certain cases the set of retrieved information items can be too large. Then we propose a probabilistic least common subsumer operation based on a probabilistic extension of the description logic language ALN. We show that by this operator the amount of retrieved data can be reduced avoiding information flood.