An unsupervised model for exploring hierarchical semantics from social annotations

  • Authors:

  • Mianwei Zhou;Shenghua Bao;Xian Wu;Yong Yu

  • Affiliations:

  • APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;IBM China Research Lab and APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;APEX Data and Knowledge Management Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China

  • Venue:
  • ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
  • Year:
  • 2007

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Abstract

This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's efficiency.