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Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments

Remi Tournaire 1, 2 Alexandre Termier 3 Jean-Marc Petit 1 Marie-Christine Rousset 3
1 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 HADAS - Heterogeneous and Adaptive distributed DAta management Systems
LIG - Laboratoire d'Informatique de Grenoble [2007-2015]
Abstract : In this paper, we investigate a principled approach for de?ning 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.
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Contributor : Fabrice Jouanot <>
Submitted on : Friday, June 6, 2014 - 3:43:46 PM
Last modification on : Thursday, July 9, 2020 - 9:44:39 AM


  • HAL Id : hal-01002693, version 1


Remi Tournaire, Alexandre Termier, Jean-Marc Petit, Marie-Christine Rousset. Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments. Journées Bases de Données Avancées (BDA 2009), 2009, namur, Belgium, Belgium. pp.1--12. ⟨hal-01002693⟩



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