Extending RCA algorithm to consider ternary relations

International audience Nowadays, in conventional agriculture, alternative solutions to pesticides, antimicrobials and other pest control products are desired. The European Green Deal is one of the most important actions Europe is taking in order to overcome the challenges of climate change and environmental degradation, sometimes caused by the usage of pesticides. In the scientific literature and local knowledge, there is a plethora of descriptions of active plant-based products used as bio-pesticides. The Knomana (KNOwledge MANAgement on pesticide plants in Africa) project's goal is to gather data about these biopesticides and implement methods to support the exploration of knowledge by the potential users (farmers, researchers, retailers, etc.). Considering the needs expressed by the domain experts, information retrieval is needed to obtain relevant insight on the matter. In addition, data clustering into similar groups is helpful when it comes to understanding key differences (or similarities) of objects in general. Formal Concept Analysis (FCA) appears as a suitable approach, due to its inherent qualities for structuring and classifying data through conceptual structures that provide a relevant support for data exploration. The Knomana data model is an entity-relationship model, including both binary and ternary relationships between entities of different categories. This hints us to explore the use of Relational Concept Analysis (RCA), an extension of FCA, on these data. In this work, we explore an extended RCA method to be able to consider ternary relations, and study a way to efficiently represent the extracted data.