Hierarchical topic term extraction for semantic annotation in chinese bulletin board system

  • Authors:

  • Xiaoyuan Wu;Shen Huang;Jie Zhang;Yong Yu

  • Affiliations:

  • Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • ASWC'06 Proceedings of the First Asian conference on The Semantic Web
  • Year:
  • 2006

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Abstract

With the current growing interest in the Semantic Web, the demand for ontological data has been on the verge of emergency Currently many structured and semi-structured documents have been applied for ontology learning and annotation However, most of the electronic documents on the web are plain-text, and these texts are still not well utilized for the Semantic Web In this paper, we propose a novel method to automatically extract topic terms to generate a concept hierarchy from the data of Chinese Bulletin Board System (BBS), which is a collection of plain-text In addition, our work provides the text source associated with the extracted concept as well, which could be a perfect fit for the semantic search application that makes a fusion of both formal and implicit semantics The experimental results indicate that our method is effective and the extracted concept hierarchy is meaningful.