The massive growth of the Internet has resulted in increased difficulty in organizing and searching through the information present on it. Several strategies have been employed to tackle this problem. With the coming of Web2.0, user-created data and sharing of information among peers, community-based tagging or folksonomy has become the norm for organization of data on the Internet. The loose structure of folksonomy has led to ease in categorization of the huge amounts of information but has also given rise to some serious problems. Users tag information based on their own experiences, preferences and common sense. This leads to difficulties in search and organization of information in the long run. In this paper, we argue that since user common sense has generated both folksonomy and a corpus of machine common sense, it seems appropriate that machine common sense be used for addressing the problem of search in folksonomy. We outline an architecture for such an operation, develop a prototype as a proof of concept and describe how this approach can be extended in the future.
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