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Conference or Workshop Item

Web Search Disambiguation by Collaborative Tagging
http://jb4-2.eprints-hosting.org/2274

Existing Web search engines such as Google mostly adopt a keyword-based approach, which matches the keywords in a query submitted by a user with the keywords characterising the indexed Web documents, and is quite successful in general in helping users locate useful documents. However, when the keyword submitted by the user is ambiguous, the search result usually consists of documents related to various 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 tagging in the social bookmarking site del.icio.us. For an ambiguous word, we extract sets of tags which are related to it in different 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.

Ching Man Au Yeung
Nicholas Gibbins
Nigel Shadbolt

User-induced Links in Collaborative Tagging Systems
http://jb4-2.eprints-hosting.org/2273

Collaborative tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in collaborative tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents.

Ching Man Au Yeung
Nicholas Gibbins
Nigel Shadbolt

This list was generated on Sun Aug 25 18:28:35 2019 UTC.