Information Retrieval in Trust-Enhanced Document Networks

  • Klaus Stein
  • Claudia Hess
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4289)


To fight the problem of information overload in huge information sources like large document repositories, e. g. citeseer, or internet websites you need a selection criterion: some kind of ranking is required. Ranking methods like PageRank analyze the structure of the document reference network. However, these rankings do not distinguish different reference semantics. We enhance these rankings by incorporating information of a second layer: the author trust network to improve ranking quality and to enable personalized selections.


Information Retrieval Visibility Function Edge Weight Trust Information Trust Network 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Klaus Stein
    • 1
  • Claudia Hess
    • 1
  1. 1.Laboratory for Semantic Information TechnologyBamberg University 

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