Tightly coupled probabilistic description logic programs for the Semantic Web

Abstract. We present a novel approach to probabilistic description logic pro-grams for the Semantic Web in which disjunctive logic programs under the an-swer set semantics are tightly coupled with description logics and Bayesian prob-abilities. The approach has several nice features. In particular, it is a logic-based representation formalism that naturally fits into the landscape of semantic web languages. Tightly coupled probabilistic description logic programs can espe-cially be used for representing mappings between ontologies, which are a com-mon way of approaching the semantic heterogeneity problem on the Semantic Web. In this application, they allow in particular for resolving inconsistencies and for merging mappings from different matchers based on the level of confidence assigned to different rules. Furthermore, tightly coupled probabilistic description logic programs also provide a natural integration of ontologies, action languages, and Bayesian probabilities towards web services. We explore the computational aspects of consistency checking and query processing in tightly coupled proba-bilistic description logic programs. We show that these problems are decidabl