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)


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.


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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  1. 1.
    Caraballo, S.A.: Automatic construction of a hypernym-labeled noun hierarchy from text. In: ACL (1999)Google Scholar
  2. 2.
    Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. J. Artif. Intell. Res (JAIR) 24, 305–339 (2005)zbMATHGoogle Scholar
  3. 3.
    Faure, D., Poibeau, T.: First experiments of using semantic knowledge learned by asium for information extraction task using intex (2000)Google Scholar
  4. 4.
    Grefenstette, G.: Explorations in automatic thesaurus construction. Kluwer, Dordrecht (1994)Google Scholar
  5. 5.
    Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. Number S2K-92-09, 8 (1992)Google Scholar
  6. 6.
    Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intelligent Systems 16(2), 72–79 (2001)CrossRefGoogle Scholar
  7. 7.
    Moldovan, D.I., Girju, R.: An interactive tool for the rapid development of knowledge bases, 65–86 (2001)Google Scholar
  8. 8.
    Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowl. Eng. Rev. 18(2), 95–145 (2003)CrossRefGoogle Scholar
  9. 9.
    Sayed, A.E., Hacid, H., Zighed, D.: Combining text and image for content-based information retrieval. In: Proceedings of the International Conference on Information and Knowledge Engineering IKE 2007 (2007)Google Scholar
  10. 10.
    Sayed, A.E., Hacid, H., Zighed, D.: A multisource context-dependent approach for semantic distance between concepts. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 54–63. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Smith, T., Waterman, M.: Identification of common molecular subsequences 195–197 (1981)Google Scholar

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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