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Parameter Learning for Probabilistic Ontologies

  • Fabrizio Riguzzi
  • Elena Bellodi
  • Evelina Lamma
  • Riccardo Zese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7994)

Abstract

Recently, the problem of representing uncertainty in Description Logics (DLs) has received an increasing attention. In probabilistic DLs, axioms contain numeric parameters that are often difficult to specify or to tune for a human. In this paper we present an approach for learning and tuning the parameters of probabilistic ontologies from data. The resulting algorithm, called EDGE, is targeted to DLs following the DISPONTE approach, that applies the distribution semantics to DLs.

Keywords

Association Rule Description Logic Binary Decision Diagram APRIORI Algorithm Distribution Semantic 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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