NICE-Food KG: A knowledge graph for the analysis of the Nutritional, Ingredient, Contaminant, and Environmental characteristics of food for food system research

• Interdisciplinary food data was integrated into one queryable system • Dutch food data was FAIRified using ontologies • Multiple food data sources can be queried in one action • Three use cases using NICE-Food KG were developed by and for food researchers Food and planetary health are interconnected, as the food system is responsible for 26% of greenhouse gas emissions while simultaneously necessary for optimal human health. Optimization and balancing of this relationship in our current and future foods is an active area of research. Integrating different data sources for this purpose remains difficult due to the heterogeneity of food data. To support food experts in complex interdisciplinary information retrieval we developed NICE-Food KG, a knowledge graph and data pipeline integrating data on N utrition, I ngredients, C ontaminants and E nvironmental impact for the Netherlands. Through data annotation and mapping employing food related ontologies and Resource Description Framework (RDF), NICE-Food KG enhances the FAIR (Findable, Accessible, Interoperable, Reusable) principles of food data. We used NICE-Food KG 1) to assess the data overlap between the different NICE domains 2) for the identification of communal food groups across the integrated data 3) to provide food recommendation based on specific food preferences 4) to infer knowledge on contemporary branded products such as meat and fish alternatives. Although more data is needed and expansions of ontologies are required to confidently bridge the gap between different disciplines in food sciences, we provide a proof of concept of how a knowledge graph supported approach can be used to integrate interdisciplinary food data, harnessing the value of FAIR data.