Collaborative Computing: Networking, Applications and Worksharing. 4th International Conference, CollaborateCom 2008, Orlando, FL, USA, November 13-16, 2008, Revised Selected Papers

Research Article

Automatic Categorization of Tags in Collaborative Environments

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  • @INPROCEEDINGS{10.1007/978-3-642-03354-4_48,
        author={Qihua Wang and Hongxia Jin and Stefan Nusser},
        title={Automatic Categorization of Tags in Collaborative Environments},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 4th International Conference, CollaborateCom 2008, Orlando, FL, USA, November 13-16, 2008, Revised Selected Papers},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={5},
        keywords={Tagging Web 2.0 Social Network Categorization Machine Learning},
        doi={10.1007/978-3-642-03354-4_48}
    }
    
  • Qihua Wang
    Hongxia Jin
    Stefan Nusser
    Year: 2012
    Automatic Categorization of Tags in Collaborative Environments
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-642-03354-4_48
Qihua Wang1, Hongxia Jin2, Stefan Nusser2
  • 1: Purdue University
  • 2: IBM Almaden Research Center

Abstract

Tagging allows individuals to use whatever terms they think are appropriate to describe an item. With the growing popularity of tagging, more and more tags have been collected by a variety of applications. An item may be associated with tags describing its different aspects, such as appearance, functionality, and location. However, little attention has been paid in the organization of tags; in most tagging systems, all the tags associated with an item are listed together regardless of their meanings. When the number of tags becomes large, finding useful information with regards to a certain aspect of an item becomes difficult. Improving the organization of tags in existing tagging systems is thus highly desired. In this paper, we propose a hierarchical approach to organize tags. In our approach, tags are placed into different categories based on their meanings. To find information with respect to a certain aspect of an item, one just needs to refer to its associated tags in the corresponding category. Since existing applications have already collected a large number of tags, manually categorizing all the tags is infeasible. We propose to use data-mining and machine-learning techniques to automatically and rapidly classify tags in tagging systems. A prototype of our approaches has been developed for a real-word tagging system.