Abstract
Recently, Bayesian extensions of Description Logics, and in particular the logic \(\mathcal{BE{\kern-.1em}L}\), were introduced as a means of representing certain knowledge that depends on an uncertain context. In this paper we introduce a novel structure, called proof structure, that encodes the contextual information required to deduce subsumption relations from a \(\mathcal{BE{\kern-.1em}L}\) knowledge base. Using this structure, we show that probabilistic reasoning in \(\mathcal{BE{\kern-.1em}L}\) can be reduced in polynomial time to standard Bayesian network inferences, thus obtaining tight complexity bounds for reasoning in \(\mathcal{BE{\kern-.1em}L}\).
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Ceylan, İ.İ., Peñaloza, R. (2014). Tight Complexity Bounds for Reasoning in the Description Logic \(\mathcal{BE{\kern-.1em}L}\) . In: Fermé, E., Leite, J. (eds) Logics in Artificial Intelligence. JELIA 2014. Lecture Notes in Computer Science(), vol 8761. Springer, Cham. https://doi.org/10.1007/978-3-319-11558-0_6
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DOI: https://doi.org/10.1007/978-3-319-11558-0_6
Publisher Name: Springer, Cham
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