Effects of input data formalisation in relational concept analysis for a data model with a ternary relation

Keip Priscilla, Gutierrez Alain, Huchard Marianne, Le Ber Florence, Sarter Samira, Silvie Pierre, Martin Pierre. 2019. Effects of input data formalisation in relational concept analysis for a data model with a ternary relation. In : Formal concept analysis: 15th International Conference, ICFCA 2019 Frankfurt, Germany, June 25–28, 2019 Proceedings. Cristea Diana (ed.), Le Ber Florence (ed.), Sertkaya Baris (ed.). Cham : Springer, 191-207. (Lecture Notes in Artificial Intelligence, 11511) ISBN 978-3-030-21461-6 International Conference on Formal Concept Analysis (ICFCA 2019). 15, Francfort, Allemagne, 25 Juin 2019/28 Juin 2019.

https://doi.org/10.1007/978-3-030-21462-3_13

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Résumé : 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.

Mots-clés Agrovoc : biopesticide, recherche de l'information, logiciel, analyse de données, informatique

Mots-clés complémentaires : Algorithme

Classification Agris : U10 - Informatique, mathématiques et statistiques
C30 - Documentation et information
H01 - Protection des végétaux - Considérations générales

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