Explaining Reference Reconciliation Decisions: a Coloured Petri Nets based Approach

Souhir Gahbiche 1 Nathalie Pernelle 2, 3 Fatiha Saïs 2, 3, *
* Auteur correspondant
3 LEO - Distributed and heterogeneous data and knowledge
UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Data integration systems aims at facilitating the management of heterogeneous data sources. When huge amount of data have to be integrated, resorting to human validations is not possible. However, completely automatic integration methods may give rise to decision errors and to approximated results. Hence, such systems need explanation modules to enhance the user confidence in the integrated data. In this paper, we focus our study on reference reconciliation methods which compare data descriptions to decide whether they refer to the same real world en- tity. Numerical reference reconciliation methods that are global and ontology driven exploit semantic knowledge to model the dependencies between similarities and to propagate them to other references. In order to explain the similarity scores and the reconciliation decisions obtained by such methods, we have developed an explanation model based on Coloured Petri Nets which provides graphical and comprehensive explanations to the user. This model allows to show the relevance of one decision, and to diagnose possible anomalies in the domain knowledge or in the similarity measures that are used.
Type de document :
Chapitre d'ouvrage
F. Guillet, G. Ritschard, D. Zighed and H. Briand (eds). Advances in Knowledge Discovery and Management, Springer Berlin / Heidelberg, 2010
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https://hal.inria.fr/inria-00519667
Contributeur : Fatiha Saïs <>
Soumis le : mardi 21 septembre 2010 - 10:35:09
Dernière modification le : jeudi 5 avril 2018 - 12:30:08

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  • HAL Id : inria-00519667, version 1

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Souhir Gahbiche, Nathalie Pernelle, Fatiha Saïs. Explaining Reference Reconciliation Decisions: a Coloured Petri Nets based Approach. F. Guillet, G. Ritschard, D. Zighed and H. Briand (eds). Advances in Knowledge Discovery and Management, Springer Berlin / Heidelberg, 2010. 〈inria-00519667〉

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