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A Hybrid Approach for Taxonomy Learning from Text

In: Compstat 2008

Author

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  • Ahmad El Sayed

    (University of Lyon 2, ERIC Laboratory)

  • Hakim Hacid

    (University of New South Wales)

Abstract

Ontology learning from text is considered as an appealing and challeging alternative 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 distributionnal approaches. It addresses key issues in the area of ontology learning: context-dependency, low recall of the pattern-based approach, low precision of the distributionnal approach, and finally ontology evolution. Experiments performed at each stage of the learning process show the advantages and drawbacks of the proposal.

Suggested Citation

  • Ahmad El Sayed & Hakim Hacid, 2008. "A Hybrid Approach for Taxonomy Learning from Text," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 255-266, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2084-3_21
    DOI: 10.1007/978-3-7908-2084-3_21
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