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Concept Learning for \(\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}}\) by Refinement and Reinforcement

  • Mahsa Chitsaz
  • Kewen Wang
  • Michael Blumenstein
  • Guilin Qi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7458)

Abstract

Ontology construction in OWL is an important and yet time-consuming task even for knowledge engineers and thus a (semi-) automatic approach will greatly assist in constructing ontologies. In this paper, we propose a novel approach to learning concept definitions in \(\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}} \) from a collection of assertions. Our approach is based on both refinement operator in inductive logic programming and reinforcement learning algorithm. The use of reinforcement learning significantly reduces the search space of candidate concepts. Besides, we present an experimental evaluation of constructing a family ontology. The results show that our approach is competitive with an existing learning system for \(\ensuremath{\cal E}\ensuremath{\cal L}\).

Keywords

Concept Learning Description Logic \(\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}}\) Reinforcement Learning Refinement Operator 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mahsa Chitsaz
    • 1
    • 2
  • Kewen Wang
    • 1
    • 2
  • Michael Blumenstein
    • 1
    • 2
  • Guilin Qi
    • 1
    • 2
  1. 1.School of Information and Communication TechnologyGriffith UniversityAustralia
  2. 2.School of Computer Science and EngineeringSoutheast UniversityChina

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