A Hybrid Approach for Taxonomy Learning from Text

Ahmad El Sayed () and Hakim Hacid ()
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Ahmad El Sayed: University of Lyon 2, ERIC Laboratory
Hakim Hacid: University of New South Wales

A chapter in COMPSTAT 2008, 2008, pp 255-266 from Springer

Abstract: 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.

Keywords: taxonomy learning; knowledge acquisition; relevance feedback (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_21

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DOI: 10.1007/978-3-7908-2084-3_21

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