Ontologies and Case Studies.

Ontologies define the main concepts and relations of a domain and can play the role of common language between domain experts, software developers and computer systems, allowing for easier and more comprehensive data management. Ontologies can provide a structure and context for data, enabling better analysis and decision‐making. Ontologies can be leveraged for improving various Machine Learning‐based tasks (they can be used for improving the accuracy and consistency of training data, and we can combine ML‐based predictions with ontology‐based reasoning). Ontologies are key components for achieving semantic data integration. In the context of this deliverable, we have surveyed 40 ontologies and 7 other knowledge organization systems related to food safety and we have categorized them according to a set of appropriate criteria. Subsequently we analysed the 18 case studies, that could involve ontologies, and for each one we have described the possible use of ontologies and what would be the benefit. Finally the identified case studies have been evaluated with respect to a set of criteria regarding benefits, cost and maturity. This publication is linked to the following EFSA Supporting Publications articles: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2024.EN-9118/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2024.EN-9119/full This publication is linked to the following EFSA Journal article: http://onlinelibrary.wiley.com/doi/10.2903/j.efsa.2024.e221201/full [ABSTRACT FROM AUTHOR]