Extract Reliable Relations from Wikipedia Texts for Practical Ontology Construction

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Auteur principal: Jin-Xia Huang
Format: Artículo científico
Langue:en
Publié: Instituto Politécnico Nacional 2016
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author Jin-Xia Huang
author_facet Jin-Xia Huang
contents Extract Reliable Relations from Wikipedia Texts for Practical Ontology Construction Jin-Xia Huang Kyung Soon Lee Key-Sun Choi Young-Kil Kim Computación based based feature feature ontology building A feature based relation classification approach is presented in this paper. We aimed to exact relation candidates from Wikipedia texts. A probabilistic and a semantic relatedness features are employed with other linguistic information for the purpose. The experiments show that, relation classification using the proposed relatedness features with surface information like word and part-of-speech tags is competitive with or even outperforms the one of using deep syntactic information. Meanwhile, an approach is proposed to distinguish reliable relation candidates from others, so that these reliable results can be accepted for knowledge building without human verification. The experiments show that, with the relation classification approach presented in this paper, more than 40% of the classification results are reliable, which means, at least 40% of the human and time costs can be saved in practice. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469015 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20
format Artículo científico
id redalyc_61547469015
institution Redalyc
language en
publishDate 2016
publisher Instituto Politécnico Nacional
spellingShingle Extract Reliable Relations from Wikipedia Texts for Practical Ontology Construction
Jin-Xia Huang
Computación
based
based
feature
feature
ontology building
Extract Reliable Relations from Wikipedia Texts for Practical Ontology Construction Jin-Xia Huang Kyung Soon Lee Key-Sun Choi Young-Kil Kim Computación based based feature feature ontology building A feature based relation classification approach is presented in this paper. We aimed to exact relation candidates from Wikipedia texts. A probabilistic and a semantic relatedness features are employed with other linguistic information for the purpose. The experiments show that, relation classification using the proposed relatedness features with surface information like word and part-of-speech tags is competitive with or even outperforms the one of using deep syntactic information. Meanwhile, an approach is proposed to distinguish reliable relation candidates from others, so that these reliable results can be accepted for knowledge building without human verification. The experiments show that, with the relation classification approach presented in this paper, more than 40% of the classification results are reliable, which means, at least 40% of the human and time costs can be saved in practice. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469015 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20
title Extract Reliable Relations from Wikipedia Texts for Practical Ontology Construction
topic Computación
based
based
feature
feature
ontology building
url https://www.redalyc.org/articulo.oa?id=61547469015