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<datestamp>2016-05-28</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Finding Semi-Automatically a Greatest Common Model Thanks to Formal Concept Analysis</title>
<creator>Amar, Bastien</creator>
<creator>Osman Guédi, Abdoulkader</creator>
<creator>Miralles, André</creator>
<creator>Huchard, Marianne</creator>
<creator>Libourel Rouge, Thérèse</creator>
<creator>Nebut, Clementine</creator>
<contributor>Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - AgroParisTech (AgroParisTech) - Irstea</contributor>
<contributor>ADVanced Analytics for data SciencE (ADVANSE) ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS) - Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Models And Reuse Engineering, Languages (MAREL) ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS) - Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Espace pour le Développement (UMR ESPACE-DEV) ; Institut de Recherche pour le Développement (IRD) - Université des Antilles et de la Guyane (UAG) - Université de la Réunion - Université de Montpellier (UM)</contributor>
<description>International audience</description>
<source>14th International Conference on Enterprise Information Systems (ICEIS 2012)</source>
<source>ICEIS: International Conference on Enterprise Information Systems</source>
<coverage>Wroclaw, Poland</coverage>
<identifier>lirmm-01319858</identifier>
<identifier>http://hal-lirmm.ccsd.cnrs.fr/lirmm-01319858</identifier>
<source>http://hal-lirmm.ccsd.cnrs.fr/lirmm-01319858</source>
<source>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></source>
<identifier>DOI : 10.1007/978-3-642-40654-6_5</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-40654-6_5</relation>
<language>en</language>
<subject>[INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>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.</description>
<date>2012-06-28</date>
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