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Currently in the Internet many collaborative tagging sites exist, but there is the need for a service to integrate the data from the multiple sites to form a large and unified set of collaborative data from which users can have more accurate and richer information than from a single site. We have proposed a collective collaborative tagging (CCT) service architecture in which both service providers and individual users can merge folksonomy data (in the form of keyword tags) stored in different sources to build a larger, unified repository. We have also examined a range of algorithms that can be applied to different problems in folksonomy analysis and information discovery. These algorithms address several common problems for online systems – searching, getting recommendations, finding communities of similar users, and finding interesting new information by trends – and also computational challenges posed by the huge amount of Internet data. Our contributions are to a) systematically examine the available public algorithms' application to tag-based folksonomies, and b) to propose a service architecture that can provide these algorithms as online capabilities.






Our system will provide the following 4 types of services:
  • Type I – Search by tag: Given input tags, returning the most relevant X (X = URLs, tags, or users)
  • Type II – Recommendation: Indirect input tags, returning undiscovered X
  • Type III – Clustering analysis: Community discovering. Finding a group or a community with similar interests
  • Type IV – Trend detection: Analysis the tagging activities in time-series manner and detect abnormality