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Mining fuzzy domain ontology based on concept vector from Wikipedia Category Network
Abstract Ontology is essential in the formalizationOntology 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.y accuracy in information retrieval tasks.
Abstractsub Ontology is essential in the formalizationOntology 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.y accuracy in information retrieval tasks.
Bibtextype inproceedings  +
Doi 10.1109/WI-IAT.2011.140  +
Has author Lu C.-Y. + , Ho S.-W. + , Chung J.-M. + , Hsu F.-Y. + , Lee H.-M. + , Ho J.-M. +
Has extra keyword Concept vector + , Domain ontologies + , Expert finding + , Reviewer classification + , Wikipedia + , Data processing + , Experiments + , Information retrieval + , Intelligent agents + , Knowledge management + , User interfaces + , Ontology +
Has keyword Concept vector + , Domain ontology + , Expert finding + , Reviewer classification + , Data mining +
Isbn 9780769545134  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 249–252  +
Published in Proceedings - 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011 +
Title Mining fuzzy domain ontology based on concept vector from Wikipedia Category Network +
Type conference paper  +
Volume 3  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 8 November 2014 03:18:30  +
Categories Duplicate publication  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 03:18:30  +
DateThis property is a special property in this wiki. 2011  +
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