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A New Framework for Taxonomy Discovery from Text

  • Ahmad El Sayed
  • Hakim Hacid
  • Djamel Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)

Abstract

Ontology learning from text is considered as an appealing and a challenging approach to address the shortcomings of the hand-crafted ontologies. In this paper, we present OLEA, a new framework for ontology learning from text. The proposal is a hybrid approach combining the pattern-based and the distributional approaches. It addresses key issues in the area of ontology learning: low recall of the pattern-based approach, low precision of the distributional approach, and finally ontology evolution. Preliminary experiments performed at each stage of the learning process show the pros and cons of the proposal.

Keywords

Semantic Relation Relevance Feedback Semantic Distance Formal Concept Analysis Text Corpus 
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 2008

Authors and Affiliations

  • Ahmad El Sayed
    • 1
  • Hakim Hacid
    • 2
  • Djamel Zighed
    • 1
  1. 1.University of Lyon 2BronFrance
  2. 2.University of New South WalesSydneyAustralia

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