Abstract: Fagging information created by users is important to understand the Web resource semantics and to improve the intelligence of Web applications. Probabilistic topic model was exploited to deal with the incompleteness and inconsistence of tagging systems. A probabilistic topic model generating technique based on tag statistical characteristics was proposed. According to tag statistical characteristics of each resource, tag documents with different format can be created. By analyzing the performance generated by different tag documents, document format that is appropriate for a certwin dataset was confirmed. High relatedness between the vocabularies in the same topic was exploited to predicate the tag for resources with incomplete and inconsistence tags. Experiments on DeliciousT 140 and Wiki10+ show the effectiveness of the technique proposed.

Key words: Tagging system, Tag predication, Statistical topic model