Food quality decision tree based on collective know-how (Capex ontology)

Agri-food chain processes are based on a multitude of knowledge, know-how and experiences forged over time. Improving food quality must go through the sharing of collective expertise. In this dataset, we provide files associated with the design and implementation of a comprehensive methodology to create a knowledge base integrating the collective expertise and use it to recommend technical actions to be taken to improve food quality. We propose an original core ontology expressed with the international languages of the Semantic Web to represent, on the one hand, knowledge in the form of decision trees representing potential causal relations between situations of interest and, on the other hand, recommendations in terms of technological actions to manage them. An example of decision tree is provided: Excessive salting in mind mapping format and RDF format. An additional Excel file contains data used to assess the relevance of the technical action's efficiency indicator.