Finding Semi-Automatically a Greatest Common Model Thanks to Formal Concept Analysis

Abstract : Data integration and knowledge capitalization combine data and information coming from different data sources designed by different experts having different purposes. In this paper, we propose to assist the underlying model merging activity. For close models made by experts of various specialities on the same system, we partially automate the identification of a Greatest Common Model (GCM) which is composed of the common concepts (core-concepts) of the different models. Our methodology is based on Formal Concept Analysis which is a method of data analysis based on lattice theory. A decision tree allows to semiautomatically classify concepts from the concept lattices and assist the GCM extraction. We apply our approach on the EIS-Pesticide project, an environmental information system which aims at centralizing knowledge and information produced by different research teams.
Type de document :
Communication dans un congrès
ICEIS: International Conference on Enterprise Information Systems, Jun 2012, Wroclaw, Poland. 14th International Conference on Enterprise Information Systems (ICEIS 2012), 141, pp.72-91, 2013, Lecture Notes in Business Information Processing. 〈10.1007/978-3-642-40654-6_5〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01319858
Contributeur : Isabelle Gouat <>
Soumis le : lundi 23 mai 2016 - 10:20:35
Dernière modification le : lundi 22 octobre 2018 - 09:54:03

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Bastien Amar, Abdoulkader Osman Guédi, André Miralles, Marianne Huchard, Thérèse Libourel Rouge, et al.. Finding Semi-Automatically a Greatest Common Model Thanks to Formal Concept Analysis. ICEIS: International Conference on Enterprise Information Systems, Jun 2012, Wroclaw, Poland. 14th International Conference on Enterprise Information Systems (ICEIS 2012), 141, pp.72-91, 2013, Lecture Notes in Business Information Processing. 〈10.1007/978-3-642-40654-6_5〉. 〈lirmm-01319858〉

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