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

  • Bastien Amar
  • Abdoulkader Osman Guédi
  • André Miralles
  • Marianne Huchard
  • Thérèse Libourel
  • Clémentine Nebut
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 141)

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 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 research teams.

Keywords

Formal concept analysis Greatest common model Information system Environmental information system Model factorization Core-concept Measuring station Pesticide 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bastien Amar
    • 1
  • Abdoulkader Osman Guédi
    • 1
    • 2
    • 3
  • André Miralles
    • 1
    • 3
  • Marianne Huchard
    • 3
  • Thérèse Libourel
    • 4
  • Clémentine Nebut
    • 3
  1. 1.Tetis/IRSTEAMaison de la TélédétectionMontpellier Cedex 5France
  2. 2.Université de DjiboutiDjiboutiDjibouti (REP)
  3. 3.LIRMMUniversity of Montpellier 2 et CNRS Montpellier Cedex 5France
  4. 4.Espace DevMaison de la Télédétection Montpellier Cedex 5France

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