Learning Probabilistic Description Logics: A Framework and Algorithms

  • José Eduardo Ochoa-Luna
  • Kate Revoredo
  • Fábio Gagliardi Cozman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)


Description logics have become a prominent paradigm in knowledge representation (particularly for the Semantic Web), but they typically do not include explicit representation of uncertainty. In this paper, we propose a framework for automatically learning a Probabilistic Description Logic from data. We argue that one must learn both concept definitions and probabilistic assignments. We also propose algorithms that do so and evaluate these algorithms on real data.


Description Logic Inductive Logic Programming Probabilistic Inclusion Conditional Mutual Information Candidate Concept 
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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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José Eduardo Ochoa-Luna
    • 1
  • Kate Revoredo
    • 2
  • Fábio Gagliardi Cozman
    • 1
  1. 1.Escola PolitécnicaUniversidade de São PauloSão PauloBrazil
  2. 2.Departamento de Informática AplicadaUnirioRio de JaneiroBrazil

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