Exploring Semantic Characteristics of Socially Constructed Knowledge Repository to Optimize Web Search
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Short-term queries preferred by most users often result in a list of Web search results with low precision from a user perspective. The purpose of this research is to improve the relevance of Web search results via search-term disambiguation and ontological filtering of search results based on socially constructed search concepts. A Special Search Browser (SSB) is developed where semantic characteristics of the socially constructed knowledge repository are extracted to form a category-document set. kNN is employed with the extracted category-documents as training data to classify Web results. Users’ selected categories are employed to present the search results. Experimental results based on five experts’ judgments over 250 hits from Yahoo! API demonstrate that utilizing the socially constructed search concepts to categorize and filter search results can improve precision by 23.5%, from Yahoo’s 41.7% to 65.2% of SSB based on the results of five selected ambiguous search-terms.
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