A new framework for taxonomy discovery from text

Authors:
Ahmad El Sayed;Hakim Hacid;Djamel Zighed
Affiliations:
University of Lyon 2, Bron, France;University of New South Wales, Sydney, NSW, Australia;University of Lyon 2, Bron, France
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

Citing 6
Cited 0

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Abstract

Ontology learning from text is considered as an appealing and a challenging approach to address the shortcomings of the handcrafted 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.