Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/26654

TitlePARNT: A statistic based approach to extract non-taxonomic relationships of ontologies from text
Author(s)Serra, Ivo
Girardi, Rosário
Novais, Paulo
KeywordsLearning non-taxonomic relationships
Ontology
Ontology learning
Natural language processing
Machine learning
Issue date2013
PublisherIEEE
Abstract(s)Learning Non-Taxonomic Relationships is a subfield of Ontology learning that aims at automating the extraction of these relationships from text. This article proposes PARNT, a novel approach that supports ontology engineers in extracting these elements from corpora of plain English. PARNT is parametrized, extensible and uses original solutions that help to achieve better results when compared to other techniques for extracting non-taxonomic relationships from ontology concepts and English text. To evaluate the PARNT effectiveness, a comparative experiment with another state of the art technique was conducted.
TypeConference paper
URIhttp://hdl.handle.net/1822/26654
ISBN978-0-7695-4967-5
DOI10.1109/ITNG.2013.70
Publisher versionhttp://dx.doi.org/10.1109/ITNG.2013.70
Peer-Reviewedyes
AccessOpen access
Appears in Collections:DI/CCTC - Artigos (papers)

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