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

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Part of the Lecture Notes in Computer Science book series (LNAI,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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Chitsaz, M., Wang, K., Blumenstein, M., Qi, G. (2012). Concept Learning for \(\ensuremath{\ensuremath{\cal E}\ensuremath{\cal L}^{++}}\) by Refinement and Reinforcement. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_4

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  • DOI: https://doi.org/10.1007/978-3-642-32695-0_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32694-3

  • Online ISBN: 978-3-642-32695-0

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