NDLI: Mining Fuzzy Domain Ontology Based on Concept Vector from Wikipedia Category Network

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Yu Lu Shou-Wei Ho Jen-Ming Chung Fu-Yuan Hsu Hahn-Ming Lee Jan-Ming Ho
Copyright Year	2011
Abstract	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.
Starting Page	249
Ending Page	252
File Size	320930
Page Count	4
File Format	PDF
ISBN	9781457713736
DOI	10.1109/WI-IAT.2011.140
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Expert-finding Electronic publishing Concept Vector Semantics Encyclopedias Ontologies Wikipedia Mining Reviewer Classification Domain Ontology Internet
Content Type	Text
Resource Type	Article

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