Abstract
Existing Web search engines such as Google mostly adopt a keyword-based approach, which matches the keywords in a query sub- mitted by a user with the keywords characterising the indexed Web doc- uments, and is quite successful in general in helping users locate useful documents. However, when the keyword submitted by the user is am- biguous, the search result usually consists of documents related to var- ious meanings of the keyword, in which probably only one of them is interesting to the user. In this paper we attempt to provide a solution to this problem by using the semantics extracted from collaborative tag- ging in the social bookmarking site del.icio.us. For an ambiguous word, we extract sets of tags which are related to it in di®erent contexts by performing a community-discovery algorithm on folksonomy networks. The sets of tags are then used to disambiguate search results returned by del.icio.us and Google. Experimental results show that our method is able to disambiguate the documents returned by the two systems with high precision.
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