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Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments

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Journal on Data Semantics XV

Part of the book series: Lecture Notes in Computer Science ((JODS,volume 6720))

Abstract

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.

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Authors and Affiliations

  1. UJF/ Grenoble INP / UPMF / CNRS, LIG UMR 5217, Université de Grenoble, 681, rue de la Passerelle, 38402, St-Martin d’Hères Cedex, France

    Rémi Tournaire, Marie-Christine Rousset & Alexandre Termier

  2. CNRS INSA-Lyon, LIRIS UMR 5205, Université de Lyon, 69621, Villeurbanne Cedex, France

    Jean-Marc Petit

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  1. Rémi Tournaire
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  2. Jean-Marc Petit
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  3. Marie-Christine Rousset
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  4. Alexandre Termier
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  1. EPFL-IC – Database Laboratory, Lausanne, Switzerland

    Stefano Spaccapietra

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Tournaire, R., Petit, JM., Rousset, MC., Termier, A. (2011). Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments. In: Spaccapietra, S. (eds) Journal on Data Semantics XV. Lecture Notes in Computer Science, vol 6720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22630-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-22630-4_3

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