Semantics and Inference for Probabilistic Description Logics

  • Riccardo Zese
  • Elena Bellodi
  • Evelina Lamma
  • Fabrizio Riguzzi
  • Fabiano Aguiari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8816)

Abstract

We present a semantics for Probabilistic Description Logics that is based on the distribution semantics for Probabilistic Logic Programming. The semantics, called DISPONTE, allows to express assertional probabilistic statements. We also present two systems for computing the probability of queries to probabilistic knowledge bases: BUNDLE and TRILL. BUNDLE is based on the Pellet reasoner while TRILL exploits the declarative Prolog language. Both algorithms compute a propositional Boolean formula that represents the set of explanations to the query. BUNDLE builds a formula in Disjunctive Normal Form in which each disjunct corresponds to an explanation while TRILL computes a general Boolean pinpointing formula using the techniques proposed by Baader and Peñaloza. Both algorithms then build a Binary Decision Diagram (BDD) representing the formula and compute the probability from the BDD using a dynamic programming algorithm. We also present experiments comparing the performance of BUNDLE and TRILL.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Riccardo Zese
    • 1
  • Elena Bellodi
    • 1
  • Evelina Lamma
    • 1
  • Fabrizio Riguzzi
    • 2
  • Fabiano Aguiari
    • 1
  1. 1.Dipartimento di IngegneriaUniversity of FerraraFerraraItaly
  2. 2.Dipartimento di Matematica e InformaticaUniversity of FerraraFerraraItaly

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