DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Model

  • Denis D. MauáEmail author
  • Fabio G. Cozman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)


The construction of probabilistic models that can represent large systems requires the ability to describe repetitive and hierarchical structures. To do so, one can resort to constructs from description logics. In this paper we present a class of relational Bayesian networks based on the popular description logic DL-Lite. Our main result is that, for this modeling language, marginal inference and most probable explanation require polynomial effort. We show this by reductions to edge covering problems, and derive a result of independent interest; namely, that counting edge covers in a particular class of graphs requires polynomial effort.



The second author was partially supported by CNPq.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Universidade de São PauloSão PauloBrazil

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