An Empirical Investigation of Learning From the Semantic Web

Peter Edwards, Gunnar Aastrand Grimnes, Alun David Preece

Research output: Contribution to conferenceUnpublished paperpeer-review

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The Semantic Web is a vision of a machine readable Web of resources, interlinked and connected through metadata with common ontologies. In this paper we explore the impact such a Semantic Web would have on Machine Learning algorithms used for user profiling and personalisation. Our hypothesis is that learning from the Semantic Web should outperform traditional learning from today’s World Wide Web for both performance and accuracy. In this paper we present results obtained with two different datasets marked-up with semantic metadata; using these we have investigated different instance representations and various learning techniques. Our initial results with the Naive Bayes and K-NN algorithms were disappointing, leading us to examine the use of the Progol algorithm. Using ILP techniques we were able to discover meaningful and we believe, potentially reusable knowledge.
Original languageEnglish
Number of pages20
Publication statusPublished - 2002
EventECML/PKDD-2002 Semantic Web Mining Workshop - Helsinki, Finland
Duration: 20 Aug 200220 Aug 2002


WorkshopECML/PKDD-2002 Semantic Web Mining Workshop


  • semantic web
  • data-mining
  • machine learning
  • web mining
  • inductive logic programming


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