Abstract
We introduce the probabilistic Description Logic \({\mathcal{BEL}}\). In \({\mathcal{BEL}}\), axioms are required to hold only in an associated context. The probabilistic component of the logic is given by a Bayesian network that describes the joint probability distribution of the contexts. We study the main reasoning problems in this logic; in particular, we (i) prove that deciding positive and almost-sure entailments is not harder for \({\mathcal{BEL}}\) than for the BN, and (ii) show how to compute the probability, and the most likely context for a consequence.
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Ceylan, İ.İ., Peñaloza, R. (2014). The Bayesian Description Logic \({\mathcal{BEL}}\) . In: Demri, S., Kapur, D., Weidenbach, C. (eds) Automated Reasoning. IJCAR 2014. Lecture Notes in Computer Science(), vol 8562. Springer, Cham. https://doi.org/10.1007/978-3-319-08587-6_37
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DOI: https://doi.org/10.1007/978-3-319-08587-6_37
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