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Using Formal Concept Analysis to Extract a Greatest Common Model

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 (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 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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Contributor : Marianne Huchard <>
Submitted on : Friday, June 12, 2020 - 1:29:10 PM
Last modification on : Wednesday, June 30, 2021 - 9:40:12 PM


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  • HAL Id : lirmm-00727009, version 1
  • IRSTEA : PUB00035895


Bastien Amar, Abdoulkader Osman Guédi, André Miralles, Marianne Huchard, Thérèse Libourel Rouge, et al.. Using Formal Concept Analysis to Extract a Greatest Common Model. 14th International Conference on Enterprise Information Systems (ICEIS), Jun 2012, Wroclaw, Poland. pp.27-37. ⟨lirmm-00727009⟩



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