Using Formal Concept Analysis to Extract a Greatest Common Model

Bastien Amar, Abdoulkader Osman Guédi, André Miralles, Marianne Huchard, Thérèse Libourel, Clémentine Nebut


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.


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Paper Citation

in Harvard Style

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 1: ICEIS, ISBN 978-989-8565-10-5, pages 27-37. DOI: 10.5220/0003996000270037

in Bibtex Style

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 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Using Formal Concept Analysis to Extract a Greatest Common Model
SN - 978-989-8565-10-5
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