Information Discovery on the World-Wide-Web

  • Ben Kao
  • David Cheung
Part of the Signals and Communication Technology book series (SCT)


This chapter discusses data-mining techniques for web-related data. In particular, we discuss techniques that can help information seekers locate relevant information on web. Two kinds of techniques, web-structure mining and web-log mining, are discussed. We also examine three techniques, authorities and hubs [10], anchor points [9], and PageRank [13] that examine the link structures of hypertext web pages. Since the web is huge and dynamic, it is not possible for any IR system to maintain a global view of the web. Recommendation of web information, therefore, has to be based on incomplete information. We discuss the idea of Internet GlOSS [2], which uses word statistics to make intelligent guess on the topics of interest of web sites. Also we discuss how the interest of web users can be abstracted in user profiles. Understanding both web users and web sites allows an effective matching of the two. Finally, we explain how mining web-log data can discover the topics of interest of web sites and user profiles.


User Profile Anchor Point User Query Conjunctive Query Part Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ben Kao
  • David Cheung

There are no affiliations available

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