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Authors: Bastien Amar 1 ; Abdoulkader Osman Guédi 2 ; André Miralles 3 ; Marianne Huchard 4 ; Thérèse Libourel 1 and Clémentine Nebut 4

Affiliations: 1 Maison de la Télédétection, France ; 2 Maison de la Télédétection, Université de Djibouti and Univ. Montpellier 2 et CNRS, France ; 3 Tetis IRSTEA, Maison de la télédetection and Univ. Montpellier 2 et CNRS, France ; 4 Univ. Montpellier 2 et CNRS, France

Keyword(s): Formal Concept Analysis, FCA, Greatest Common Model, GCM, Pesticide, Environmental Information System, Model Factorization, Core-concept, Domain-concept.

Related Ontology Subjects/Areas/Topics: Applications ; Coupling and Integrating Heterogeneous Data Sources ; Databases and Information Systems Integration ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Operational Research ; Project Management ; Software Engineering ; Tools, Techniques and Methodologies for System Development

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, we partially automate the identification of a Greatest Common Model (GCM) which is composed of the common concepts (coreconcepts) 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 semi-automatically 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 specialized teams.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Amar, B.; Osman Guédi, A.; Miralles, A.; Huchard, M.; Libourel, T. and Nebut, C. (2012). Using Formal Concept Analysis to Extract a Greatest Common Model. In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-8565-10-5; ISSN 2184-4992, SciTePress, pages 27-37. DOI: 10.5220/0003996000270037

@conference{iceis12,
author={Bastien Amar. and Abdoulkader {Osman Guédi}. and André Miralles. and Marianne Huchard. and Thérèse Libourel. and Clémentine Nebut.},
title={Using Formal Concept Analysis to Extract a Greatest Common Model},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2012},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003996000270037},
isbn={978-989-8565-10-5},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Using Formal Concept Analysis to Extract a Greatest Common Model
SN - 978-989-8565-10-5
IS - 2184-4992
AU - Amar, B.
AU - Osman Guédi, A.
AU - Miralles, A.
AU - Huchard, M.
AU - Libourel, T.
AU - Nebut, C.
PY - 2012
SP - 27
EP - 37
DO - 10.5220/0003996000270037
PB - SciTePress