Extraction of association implication rules from knowledge on plants with pesticidal and antibiotic effect classified by FCA within the One-Health initiative
Résumé
Reducing the use of pesticides and antibiotics is a major challenge to manage resistance and to provide sustainable production systems that respect the One-Health initiative [1]. It consists in conserving and enhancing biodiversity and natural resources, and proposing ecologically intensive alternatives adapted to local environmental and social conditions. Local plant extract can be used as substitutes for pesticides and antibiotics. To this end, a knowledge base including 42,000 descriptions of plant extracts uses in Africa with pesticidal, antimicrobial, and antiparasitic effect is being developed since 2018 [2]. Its purpose is to enable different categories of users (farmers, fish farmers, researchers, consultants, etc.) to identify local plants that can be used to solve plant and animal health problems. To explore and navigate in the knowledge base, knowledge is classified using formal concept analysis (FCA) [3]. Exploration of the knowledge by FCA supports the discovery of plant extracts already known as relevant treatments. It also provides, by considering partial or complete similarities between plants, pest, or chemical composition, hypotheses on new treatments to experiment. To meet the One-Health initiative, one must ensure that a proposed plant does not cause any undesirable induced effect on humans. For example, animals or plants intended for human consumption (vegetables, fishes, etc.) should not be treated with plants containing molecules already used in medical care to avoid the development of resistance. To implement this initiative, implication rules [4] are extracted from the classification to highlight, among others, the existing relationships between molecules used in medical care and the ones used in the plant extracts for animal/plant protection. This poster presents the method of construction of these implications rules extracted from the classification provided by FCA and examples of results.
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