Mining fuzzy domain ontology based on concept vector from Wikipedia Category Network
|Mining fuzzy domain ontology based on concept vector from Wikipedia Category Network|
|Author(s)||Lu C.-Y., Ho S.-W., Chung J.-M., Hsu F.-Y., Lee H.-M., Ho J.-M.|
|Published in||Proceedings - 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011|
|Keyword(s)||Concept vector, Domain ontology, Expert finding, Reviewer classification, Wikipedia mining (Extra: Concept vector, Domain ontologies, Expert finding, Reviewer classification, Wikipedia, Data processing, Experiments, Information retrieval, Intelligent agents, Knowledge management, User interfaces, Ontology)|
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Mining fuzzy domain ontology based on concept vector from Wikipedia Category Network is a 2011 conference paper written in English by Lu C.-Y., Ho S.-W., Chung J.-M., Hsu F.-Y., Lee H.-M., Ho J.-M. and published in Proceedings - 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011.
Ontology is essential in the formalization of domain knowledge for effective human-computer interactions (i.e., expert-finding). Many researchers have proposed approaches to measure the similarity between concepts by accessing fuzzy domain ontology. However, engineering of the construction of domain ontologies turns out to be labor intensive and tedious. In this paper, we propose an approach to mine domain concepts from Wikipedia Category Network, and to generate the fuzzy relation based on a concept vector extraction method to measure the relatedness between a single term and a concept. Our methodology can conceptualize domain knowledge by mining Wikipedia Category Network. An empirical experiment is conducted to evaluate the robustness by using TREC dataset. Experiment results show the constructed fuzzy domain ontology derived by proposed approach can discover robust fuzzy domain ontology with satisfactory accuracy in information retrieval tasks.
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