Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2008)
Dec. 9, 2008 to Dec. 12, 2008
Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.
collaborative tagging, folksonomy, web search, classification, knn
N. Gibbins, C. A. Yeung and N. Shadbolt, "A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies," Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on(WI-IAT), vol. 01, no. , pp. 70-76, 2008.