Abstract
We propose tractable probabilistic description logic programs (or probabilistic dl-programs) for the Semantic Web, which combine tractable description logics, normal programs under the answer set semantics, and probabilities. In particular, we introduce the total well-founded semantics for probabilistic dl-programs. 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 tight (resp., tight literal) query processing under the previous well-founded (resp., answer set) semantics whenever 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.
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Lukasiewicz, T. (2007). Tractable Probabilistic Description Logic Programs. In: Prade, H., Subrahmanian, V.S. (eds) Scalable Uncertainty Management. SUM 2007. Lecture Notes in Computer Science(), vol 4772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75410-7_11
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DOI: https://doi.org/10.1007/978-3-540-75410-7_11
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