Social Tagging Site has increasingly become a main avenue for people to share resources online. Users can use simple tools to publish everything from bookmarks to video clips on those sites. However, effective querying of resources in such sites is still a challenging question in industry and academic research. This paper reports a novel algorithm of presenting the web resources query result on the social tagging sites. It adopts a two step clustering approach to organize and rank resources based on their relative similarities with each other. Initial term similarities are computed using user and tag information of the resource. The query results are then organized as a group of concepts represented by a few semantically related terms. The resources that are related with each concept are rank with respect to the concept. In addition, concepts are also ranked by representing terms and the number of resources associated.
['Ying Zhou']
Searching and Clustering on Social Tagging Sites