Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments

  • Rémi Tournaire
  • Jean-Marc Petit
  • Marie-Christine Rousset
  • Alexandre Termier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6720)


In this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm which minimizes the number of calls to the probability estimator for determining those mappings whose probability exceeds a certain threshold. Finally, we provide an experimental analysis of this approach.


Probabilistic Mapping Directed Acyclic Graph Schema Match Ontology Match Logical Semantic 
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 2011

Authors and Affiliations

  • Rémi Tournaire
    • 1
  • Jean-Marc Petit
    • 2
  • Marie-Christine Rousset
    • 1
  • Alexandre Termier
    • 1
  1. 1.UJF/ Grenoble INP / UPMF / CNRS, LIG UMR 5217Université de GrenobleSt-Martin d’Hères CedexFrance
  2. 2.CNRS INSA-Lyon, LIRIS UMR 5205Université de LyonVilleurbanne CedexFrance

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