Simple Algorithms for Predicate Suggestions Using Similarity and Co-occurrence

  • Eyal Oren
  • Sebastian Gerke
  • Stefan Decker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)


When creating Semantic Web data, users have to make a critical choice for a vocabulary: only through shared vocabularies can meaning be established. A centralised policy prevents terminology divergence but would restrict users needlessly. As seen in collaborative tagging environments, suggestion mechanisms help terminology convergence without forcing users. We introduce two domain-independent algorithms for recommending predicates (RDF statements) about resources, based on statistical dataset analysis. The first algorithm is based on similarity between resources, the second one is based on co-occurrence of predicates. Experimental evaluation shows very promising results: a high precision with relatively high recall in linear runtime performance.


Association Rule Recommender System Outgoing Edge Runtime Performance Similar Resource 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Eyal Oren
    • 1
  • Sebastian Gerke
    • 1
  • Stefan Decker
    • 1
  1. 1.Digital Enterprise Research Institute, National University of Ireland, Galway, GalwayIreland

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