Integrating multiple internet directories by instance-based learning

  • Authors:

  • Ryutaro Ichise;Hiedeaki Takeda;Shinichi Honiden

  • Affiliations:

  • National Institute of Informatics, Chiyoda-ku, Tokyo, Japan;National Institute of Informatics, Chiyoda-ku, Tokyo, Japan;National Institute of Informatics, Chiyoda-ku, Tokyo, Japan

  • Venue:
  • IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
  • Year:
  • 2003

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Abstract

Finding desired information on the Internet is becoming increasingly difficult. Internet directories such as Yahoo!, which organize web pages into hierarchical categories, provide one solution to this problem; however, such directories are of limited use because some bias is applied both in the collection and categorization of pages. We propose a method for integrating multiple Internet directories by instance-based learning. Our method provides the mapping of categories in order to transfer documents from one directory to another, instead of simply merging two directories into one. We present herein an effective algorithm for determining similar categories between two directories via a statistical method called the k-statistic. In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The results show that the proposed method achieves extensive improvements relative to both the Naive Bayes and Enhanced Naive Bayes approaches, without any text analysis on documents.