Mining tag semantics for social tag recommendation

Nowadays lots of information are stored as Web pages for ease of sharing and searching. It is rather impractical for users to browse such gigantic amount of Web pages to obtain target information. Therefore, users often count on search engines to retrieve information they needed. Most of the search engines rely on the contents of Web pages, which are often represented by sets of keywords, to decide their relatedness to a user´s query. However, such scheme is often imprecise since there is always a semantic gap between the actual semantics of a Web page and its representation. Manual annotations by human beings will certainly help since such annotations should always reflect the true meaning of the Web pages. However, ordinary annotators may not annotate a Web page with appropriate tags and thus deteriorate the quality of tags as well as the retrieval result. In this work, we propose a novel scheme for recommending tags while a user intends to annotate a Web page. Our scheme first discover the relationships across Web pages and tags based their clustering result. We then recommend tags to users according to such relationships. We tested our work on RSDC 2008 dataset and obtained promising result.