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Autoria: • Ivo Serra
• Rosario Girardi
• Paulo Novais
Título: PARNT: A Statistic based Approach to Extract Non-Taxonomic Relationships of Ontologies from Text
Complemento: 10th International Conference on Information Technology : New Generations
Resumo: Learning Non-Taxonomic Relationships is a sub-field 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.
Palavras-chave: Learning non-taxonomic relationships; Machine learning ; Ontology learning
Local: Las Vegas, Nevada, USA
Data: 15 a 17 de abril de 2013
Meio: Anais de eventos
Vínculo: www.itng.info/
Arquivo: PARNT.zip


Atualizado em 03/11/2016
UNIVERSIDADE FEDERAL DO MARANHÃO
CENTRO DE CIÊNCIAS EXATAS E TECNOLOGIA
DEPARTAMENTO DE INFORMÁTICA