Probabilistic Description Logic Programs

Abstract

Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertainty in the Rules, Logic, and Proof layers of the Semantic Web, we present probabilistic description logic programs (or pdl-programs), which are a combination of description logic programs (or dl-programs) under the answer set semantics and the well-founded semantics with Poole's independent choice logic. We show that query processing in such pdl-programs can be reduced to computing all answer sets of dl-programs and solving linear optimization problems, and to computing the well-founded model of dl-programs, respectively. Moreover, we show that the answer set semantics of pdl-programs is a refinement of the well-founded semantics of pdl-programs. Furthermore, we also present an algorithm for query processing in the special case of stratified pdl-programs, which is based on a reduction to computing the canonical model of stratified dl-programs.