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A New Framework for Taxonomy Discovery from Text  Auteur(s): ELSAYED A., HACID H., ZIGHED D. A. Actes de conférence: Conference: Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 08), Osaka, Japan (Osaka, JP, 2008-05-20) Publié: Advances in Knowledge Discovery and Data Mining, vol. 5012/2008 (2008) p.985-991 DOI: 10.1007/978-3-540-68125-0_103 Résumé: 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. Commentaires: pakdd08shz |
