Probabilistic Query Answering in the Bayesian Description Logic \(\mathcal {BE{}L}\)

  • İsmail İlkan CeylanEmail author
  • Rafael Peñaloza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)


\(\mathcal {BE{}L}\) 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.


Bayesian Network Description Logic Reasoning Task Conjunctive Query Query Answering 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Theoretical Computer ScienceTU DresdenDresdenGermany
  2. 2.KRDB Research CentreFree University of Bozen-BolzanoBolzanoItaly

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