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First-order learning for Web mining

  • Mark Craven
  • Seán Slattery
  • Kamal Nigam
Relational Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

We present compelling evidence that the World Wide Web is a domain in which applications can benefit from using first-order learning methods, since the graph structure inherent in hypertext naturally lends itself to a relational representation. We demonstrate strong advantages for two applications — learning classifiers for Web pages, and learning rules to discover relations among pages.

Keywords

Inductive Logic Programming Background Relation Binary Classification Task Inductive Logic Program Conventional Text 
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.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Mark Craven
    • 1
  • Seán Slattery
    • 1
  • Kamal Nigam
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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