Tightly Integrated Probabilistic Description Logic Programs for the Semantic Web
We present a novel approach to probabilistic description logic programs for the Semantic Web, where a tight integration of disjunctive logic programs under the answer set semantics with description logics is generalized by probabilistic uncertainty. The approach has a number of nice features. In particular, it allows for a natural probabilistic data integration and for a natural representation of ontology mappings under probabilistic uncertainty and inconsistency. It also provides a natural integration of a situation-calculus based language for reasoning about actions with both description logics and probabilistic uncertainty.
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