Peer Selection in Peer-to-Peer Networks with Semantic Topologies

  • Peter Haase
  • Ronny Siebes
  • Frank van Harmelen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3226)


Peer-to-Peer systems have proven to be an effective way of sharing data. Modern protocols are able to efficiently route a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic topology. Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. To calculate our semantic similarity measure we make the simplifying assumption that the peers share the same ontology. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments we show how expertise based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages.


Semantic Similarity Latent Semantic Indexing Expertise Model Topic Distribution Semantic Similarity Measure 
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 2004

Authors and Affiliations

  • Peter Haase
    • 1
  • Ronny Siebes
    • 2
  • Frank van Harmelen
    • 2
  1. 1.Institute AIFBUniversity of KarlsruheKarlsruheGermany
  2. 2.Vrije Universiteit AmsterdamThe Netherlands

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