Tightly Integrated Probabilistic Description Logic Programs for Representing Ontology Mappings

Abstract

Creating mappings between ontologies is a common way of approaching the semantic heterogeneity problem on the Semantic Web. To fit into the landscape of semantic web languages, a suitable, logic-based representation formalism for mappings is needed. We argue that such a formalism has to be able to deal with uncertainty and inconsistencies in automatically created mappings. We analyze the requirements for such a formalism, and we propose a novel approach to probabilistic description logic programs as such a formalism, which tightly combines disjunctive logic programs under the answer set semantics with both description logics and Bayesian probabilities. We define the language, and we show that it can be used to resolve inconsistencies and merge mappings from different matchers based on the level of confidence assigned to different rules. Furthermore, we explore the computational aspects of consistency checking and query processing in tightly integrated probabilistic description logic programs. We show that these problems are decidable and computable, respectively, and that they can be reduced to consistency checking and cautious/brave reasoning, respectively, in tightly integrated disjunctive description logic programs. We also analyze the complexity of consistency checking and query processing in the new probabilistic description logic programs in special cases. In particular, we present a special case of these problems with polynomial data complexity.