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ArThUR: A Tool for Markov Logic Network

  • Axel Bodart
  • Keyvin Evrard
  • James Ortiz
  • Pierre-Yves Schobbens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8842)

Abstract

Logical approaches-and ontologies in particular-offer a well-adapted framework for representing knowledge present on the Semantic Web ( Open image in new window ). These ontologies are formulated in Open image in new window ( Open image in new window ), which are based on expressive Open image in new window ( Open image in new window ). Open image in new window are a subset of Open image in new window ( Open image in new window ) that provides decidable reasoning. Based on Open image in new window , it is possible to rely on inference mechanisms to obtain new knowledge from axioms, rules and facts specified in the ontologies. However, these classical inference mechanisms do not deal with : Open image in new window probabilities. Several works recently targeted those issues (i.e. Open image in new window , Open image in new window , Open image in new window , etc.), but none of them combines Open image in new window with Open image in new window ( Open image in new window ) formalism. Several open source software packages for Open image in new window are available (e.g. Open image in new window , Open image in new window , Open image in new window , etc.). In this paper, we present Open image in new window , a Java framework for reasoning with probabilistic information in the Open image in new window . Open image in new window incorporate three open source software packages for Open image in new window , which is able to reason with uncertainty information, showing that it can be used in several real-world domains. We also show several experiments of our tool with different ontologies.

Keywords

Description Logic OWL2 Ontology Markov Logic Network Open Source Software Package Weight Learning 
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 2014

Authors and Affiliations

  • Axel Bodart
    • 1
  • Keyvin Evrard
    • 1
  • James Ortiz
    • 1
  • Pierre-Yves Schobbens
    • 1
  1. 1.Computer Science FacultyUniversity of NamurBelgium

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