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Web Intelligence, IEEE / WIC / ACM International Conference on (2007)
Silicon Valley, California, USA
Nov. 2, 2007 to Nov. 5, 2007
ISBN: 0-7695-3026-5
pp: 402-408
ABSTRACT
This paper proposes a method for learning ontologies given a corpus of text documents. The method identifies concepts in documents and organizes them into a subsumption hierarchy, without presupposing the existence of a seed ontology. The method uncovers latent topics in terms of which document text is being generated. These topics form the concepts of the new ontology. This is done in a language neutral way, using probabilistic space reduction techniques over the original term space of the corpus. Given multiple sets of concepts (latent topics) being discovered, the proposed method constructs a subsumption hierarchy by performing conditional independence tests among pairs of latent topics, given a third one. The paper provides experimental results over the GENIA corpus from the domain of biomedicine.
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CITATION
Georgios Paliouras, Elias Zavitsanos, George A. Vouros, Sergios Petridis, "Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora", Web Intelligence, IEEE / WIC / ACM International Conference on, vol. 00, no. , pp. 402-408, 2007, doi:10.1109/WI.2007.55
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