A Bayesian Extension of the Description Logic \(\mathcal {ALC}\)

  • Leonard Botha
  • Thomas Meyer
  • Rafael PeñalozaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11468)


Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension \(\mathcal {BALC}\) of the DL \(\mathcal {ALC}\). We present a tableau-based procedure for deciding consistency, and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical \(\mathcal {ALC}\) .


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Cape Town and CAIRCape TownSouth Africa
  2. 2.University of Milano-BicoccaMilanoItaly

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