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A smart itsy bitsy spider for the Web

Author

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  • Hsinchun Chen
  • Yi‐Ming Chung
  • Marshall Ramsey
  • Christopher C. Yang

Abstract

As part of the ongoing Illinois Digital Library Initiative project, this research proposes an intelligent agent approach to Web searching. In this experiment, we developed two Web personal spiders based on best first search and genetic algorithm techniques, respectively. These personal spiders can dynamically take a user's selected starting homepages and search for the most closely related homepages in the Web, based on the links and keyword indexing. A graphical, dynamic, Java‐based interface was developed and is available for Web access. A system architecture for implementing such an agent‐based spider is presented, followed by detailed discussions of benchmark testing and user evaluation results. In benchmark testing, although the genetic algorithm spider did not outperform the best first search spider, we found both results to be comparable and complementary. In user evaluation, the genetic algorithm spider obtained significantly higher recall value than that of the best first search spider. However, their precision values were not statistically different. The mutation process introduced in genetic algorithm allows users to find other potential relevant homepages that cannot be explored via a conventional local search process. In addition, we found the Java‐based interface to be a necessary component for design of a truly interactive and dynamic Web agent. © 1998 John Wiley & Sons, Inc.

Suggested Citation

  • Hsinchun Chen & Yi‐Ming Chung & Marshall Ramsey & Christopher C. Yang, 1998. "A smart itsy bitsy spider for the Web," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(7), pages 604-618, May.
    • Handle: RePEc:bla:jamest:v:49:y:1998:i:7:p:604-618
    DOI: 10.1002/(SICI)1097-4571(19980515)49:73.0.CO;2-T
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    Cited by:

    1. Lebib, Fatma Zohra & Mellah, Hakima & Drias, Habiba, 2017. "Enhancing information source selection using a genetic algorithm and social tagging," International Journal of Information Management, Elsevier, vol. 37(6), pages 741-749.

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