Abstract
\(\mathcal {BE{}L}\) is a probabilistic description logic (DL) that extends the light-weight DL \(\mathcal {E{}L}\) with a joint probability distribution over the axioms, expressed with the help of a Bayesian network (BN). In recent work it has been shown that the complexity of standard logical reasoning in \(\mathcal {BE{}L}\) is the same as performing probabilistic inferences over the BN.
In this paper we consider conjunctive query answering in \(\mathcal {BE{}L}\). We study the complexity of the three main problems associated to this setting: computing the probability of a query entailment, computing the most probable answers to a query, and computing the most probable context in which a query is entailed. In particular, we show that all these problems are tractable w.r.t. data and ontology complexity.
This work was partially supported by DFG within the Research Training Group “RoSI” (GRK 1907) and the Cluster of Excellence ‘cfAED.’ Most of the work was developed while R. Peñaloza was still affiliated with TU Dresden and the Center for Advancing Electronics Dresden, Germany.
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Ceylan, İ.İ., Peñaloza, R. (2015). Probabilistic Query Answering in the Bayesian Description Logic \(\mathcal {BE{}L}\) . In: Beierle, C., Dekhtyar, A. (eds) Scalable Uncertainty Management. SUM 2015. Lecture Notes in Computer Science(), vol 9310. Springer, Cham. https://doi.org/10.1007/978-3-319-23540-0_2
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