Refining the results of automatic e-textbook construction by clustering

  • Authors:

  • Jing Chen;Qing Li;Ling Feng

  • Affiliations:

  • Department of Computer Engineering and Information Technology, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Engineering and Information Technology, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Science, University of Twente, Enschede, The Netherlands

  • Venue:
  • ICWL'05 Proceedings of the 4th international conference on Advances in Web-Based Learning
  • Year:
  • 2005

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Abstract

The abundance of knowledge-rich information on the World Wide Web makes compiling an online e-textbook both possible and necessary. The authors of [7] proposed an approach to automatically generate an e-textbook by mining the ranking lists of the search engine. However, the performance of the approach was degraded by Web pages that were relevant but not actually discussing the desired concept. In this paper, we extend the work in [7] by applying a clustering approach before the mining process. The clustering approach serves as a post-processing stage to the original results retrieved by the search engine, and aims to reach an optimum state in which all Web pages assigned to a concept are discussing that exact concept.