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Building Domain Ontology through Concept and Relation Classification  

Huang, Jin-Xia (한국과학기술원 전자전산학부)
Shin, Ji-Ae (한국정보통신대학교 전산학과)
Choi, Key-Sun (한국과학기술원 전자전산학부)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.9, 2008 , pp. 562-571 More about this Journal
Abstract
For the purpose of building domain ontology, this paper proposes a methodology for building core ontology first, and then enriching the core ontology with the concepts and relations in the domain thesaurus. First, the top-level concept taxonomy of the core ontology is built using domain dictionary and general domain thesaurus. Then, the concepts of the domain thesaurus are classified into top-level concepts in the core ontology, and relations between broader terms (BT) - narrower terms (NT) and related terms (RT) are classified into semantic relations defined for the core ontology. To classify concepts, a two-step approach is adopted, in which a frequency-based approach is complemented with a similarity-based approach. To classify relations, two techniques are applied: (i) for the case of insufficient training data, a rule-based module is for identifying isa relation out of non-isa ones; a pattern-based approach is for classifying non-taxonomic semantic relations from non-isa. (ii) For the case of sufficient training data, a maximum-entropy model is adopted in the feature-based classification, where k-NN approach is for noisy filtering of training data. A series of experiments show that performances of the proposed systems are quite promising and comparable to judgments by human experts.
Keywords
domain ontology; core ontology; thesaurus; concept; relation; classification;
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Times Cited By KSCI : 2  (Citation Analysis)
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