Extract reliable relations from wikipedia texts for practical ontology construction

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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.
Publisher
IPN
Issue Date
2016
Language
English
Article Type
Article
Citation

Computacion y Sistemas, v.20, no.3, pp.467 - 476

ISSN
1405-5546
DOI
10.13053/CyS-20-3-2454
URI
http://hdl.handle.net/10203/220441
Appears in Collection
CS-Journal Papers(저널논문)
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