›› 2011, Vol. 26 ›› Issue (5): 806-815.doi: 10.1007/s11390-011-0179-y

• Special Section on Community Analysis and Information Recommendation • Previous Articles     Next Articles

Modeling Consensus Semantics in Social Tagging Systems

Bin Zhang1 (张斌), Senior Member, CCF, Member, ACM, Yin Zhang1 (张引), and Ke-Ning Gao2 (高克宁)   

  1. 1. College of Information Science and Technology, Northeastern University, Shenyang 110004, China
    2. Computing Center, Northeastern University, Shenyang 110004, China
  • Received:2010-09-30 Revised:2011-06-20 Online:2011-09-05 Published:2011-09-05
  • Contact: Bin Zhang E-mail:zhangbin@ise.neu.edu.cn; zhangyin@research.neu.edu.cn; gkn@cc.neu.edu.cn
  • About author:Bin Zhang is a professor in the College of Information Science and Technology at Northeastern University, Shenyang, China. He is a senior member of CCF and a member of ACM. He received his Ph.D. degree from Northeastern University in 1997. His current research interests include service oriented computing and information retrieval.
    Yin Zhang received his B.S. degree in computer science from Northeastern University in 2006. He is currently a Ph.D. candidate in Northeastern University. His current research interests include information retrieval and social media.
    Ke-Ning Gao is a professor in the Computing Center at Northeastern University, Shenyang, China. She received her Ph.D. degree from Northeastern University in 2006. Her current research interests include information retrieval and social media.
  • Supported by:

    Supported by the National Natural Science Foundation of China under Grant No. 61073062, the Natural Science Foundation of Liaoning Province of China under Grant No. 20102060 and the Fundamental Research Funds for the Central Universities under Grant No. N090604010.

In social tagging systems, people can annotate arbitrary tags to online data to categorize and index them. However, the lack of the "a priori" set of words makes it difficult for people to reach consensus about the semantics of tags and how to categorize data. Ontologies based approaches can help reaching such consensus, but they are still facing problems such as inability of model ambiguous and new concepts properly. For tags that are used very few times, since they can only be used in very specific contexts, their semantics are very clear and detailed. Although people have no consensus on these tags, it is still possible to leverage these detailed semantics to model the other tags. In this paper we introduce a random walk and spreading activation like model to represent the semantics of tags using semantics of unpopular tags. By comparing the proposed model to the classic Latent Semantic Analysis approach in a concept clustering task, we show that the proposed model can properly capture the semantics of tags.

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