One of the most popular technologies in Web2.0 is tagging, and it widely applies to Web content as well as multimedia data such as image and video. Web users have expected that tags by themselves would be reused in information search and maximize the search efficiency, but wrong tag by irresponsible Web users really has brought forth a incorrect search results. In past papers, we have gathered various information resources and tags scattered in Web, mapped one tag onto other tags, and clustered these tags according to the corelation between them. A 3-tag based search algorithm which use the clustered tags of past papers, is proposed in this paper. For performance evaluation of the proposed algorithm, our algorithm is compared with image search result of Flickr, typical tag based site, and is evaluated in accuracy and recall factor.

Until recently, it was expected that the web users' tagging would be used in classification, recommendation, and information search. However, the users' subjective decision or infrequent tags with high interrelationship have caused inaccurate results.This research proposes an algorithm to disregard users' subjective tags and utilize infrequent tags with high semantic similarity using WordNet. Furthermore, to enhance the efficiency of the proposed algorithm, a weighted matrix is proposed.To evaluate the proposed algorithm, the current co-appearing frequency between tagpairs method, tag-pair semantic similarity extraction algorithm, and tag-pair weight matrix method were analyzed and compared.

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Abstract. Most information in Web2.0 is made by the users and classified by the tags which are putted by the users. However, as we known, tag related service and research are focused on the works such as automatic tagging and tag cloud composition, but the researches which classify the media resources and information according to tags and provide the results to the users, are not still up to the mark.In this paper to overcome the problems of existing system, a TBTC algorithm was proposed to find highly related tags. And a threshold which is used in this algorithm for clustering was studied, and optimal threshold with high cluster cohesion was determined in this research.