Effects of Input Data Formalisation in Relational Concept Analysis for a Data Model with a Ternary Relation

  • Priscilla KeipEmail author
  • Alain Gutierrez
  • Marianne Huchard
  • Florence Le Ber
  • Samira Sarter
  • Pierre Silvie
  • Pierre Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11511)


Today pesticides, antimicrobials and other pest control products used in conventional agriculture are questioned and alternative solutions are searched out. Scientific literature and local knowledge describe a significant number of active plant-based products used as bio-pesticides. The Knomana (KNOwledge MANAgement on pesticide plants in Africa) project aims to gather data about these bio-pesticides and implement methods to support the exploration of knowledge by the potential users (farmers, advisers, researchers, retailers, etc.). Considering the needs expressed by the domain experts, Formal Concept Analysis (FCA) appears as a suitable approach, due do its inherent qualities for structuring and classifying data through conceptual structures that provide a relevant support for data exploration. The Knomana data model used during the data collection is an entity-relationship model including both binary and ternary relationships between entities of different categories. This leads us to investigate the use of Relational Concept Analysis (RCA), a variant of FCA on these data. We consider two different encodings of the initial data model into sets of object-attribute contexts (one for each entity category) and object-object contexts (relationships between entity categories) that can be used as an input for RCA. These two encodings are studied both quantitatively (by examining the produced conceptual structures size) and qualitatively, through a simple, yet real, scenario given by a domain expert facing a pest infestation.


Biopesticides Data exploration Formal Concept Analysis Relational Concept Analysis 



This work was supported by the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 and by INRA-CIRAD GloFoodS metaprogram (KNOMANA project).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CIRAD, UPR AIDAMontpellierFrance
  2. 2.AIDA, Univ Montpellier, CIRADMontpellierFrance
  3. 3.LIRMM, Université de Montpellier, CNRSMontpellierFrance
  4. 4.ICube, Université de Strasbourg, CNRS, ENGEESIllkirch-GraffenstadenFrance
  5. 5.IRD, UMR EGCEGif-sur-YvetteFrance
  6. 6.ISEM, Univ Montpellier, CIRAD, CNRS, EPHE, IRDMontpellierFrance

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