Combining implications and conceptual analysis to learn from a pesticidal plant knowledge base

Mahrach Lina, Gutierrez Alain, Keip Priscilla, Marnotte Pascal, Silvie Pierre, Martin Pierre. 2021. Combining implications and conceptual analysis to learn from a pesticidal plant knowledge base. In : Graph-based representation and reasoning. Braun Tanya (ed.), Gehrke Marcel (ed.), Hanika Tom (ed.), Hernandez Nathalie (ed.). Cham : Springer, 57-72. (Lecture Notes in Computer Science, 12879) ISBN 978-3-030-86981-6 International Conference on Conceptual Structures (ICCS 2021). 26, s.l., 20 Septembre 2021/22 Septembre 2021.

https://doi.org/10.1007/978-3-030-86982-3_5

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Résumé : Supporting organic farming aims to find alternative solutions to synthetic pesticides and antibiotics, using local plants, to protect crops. Moreover, in the One Health approach (OHA), a pesticidal plant should not be harmful to humans, meaning it cannot be toxic if the crop is consumed or should have a limited and conscious use if it is used for medical care. Knowledge on plant use presented in the scientific literature was compiled in a knowledge base (KB). The challenge is to develop a KB exploration method that informs experts (including farmers) about protection systems properties that respect OHA. In this paper, we present a method that extracts the Duquenne-Guigues basis of implications from knowledge structured using Relational Concept Analysis (RCA). We evaluate the impact of three data representations on the implications and their readability. The experimentation is conducted on 562 plant species used to protect 15 crops against 29 pest species of the Noctuidae family. Results show that consistently splitting data into several tables fosters less redundant and more focused implications.

Mots-clés libres : Relational concept analysis, Implication rules, One Health, Knowledge base

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