Tractable Probabilistic Description Logic Programs

Abstract

We propose tractable probabilistic description logic programs (dl-programs) for the Semantic Web, which combine tractable description logics (DLs), normal programs under the answer set and the well-founded semantics, and probabilities. In detail, we first provide novel reductions of tight query processing and of deciding consistency in probabilistic dl-programs under the answer set semantics to the answer set semantics of the underlying normal dl-programs. Based on these reductions, we then introduce a novel well-founded semantics for probabilistic dl-programs, called the total well-founded semantics. Contrary to the previous answer set and well-founded semantics, it is defined for all probabilistic dl-programs and all probabilistic queries. Furthermore, tight (resp., tight literal) query processing under the total well-founded semantics coincides with (resp., approximates) tight (resp., tight literal) query processing under the previous well-founded (resp., answer set) semantics in all cases where the latter is defined. We then present an anytime algorithm for tight query processing in probabilistic dl-programs under the total well-founded semantics. We also show that tight literal query processing in probabilistic dl-programs under the total well-founded semantics can be done in polynomial time in the data complexity and is complete for EXP in the combined complexity. Finally, we describe an application of probabilistic dl-programs in probabilistic data integration for the Semantic Web.KeywordsLogic ProgramQuery ProcessingDescription LogicConjunctive QuerySource SchemaThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.