Editorial: Knowledge graph technologies: the next Frontier of the food, agriculture, and water domains

Catherine Roussey, Christophe Guéret, Marie-Angélique Laporte

Abstract: Ontologies are the back-bone of KG modeling as they define what is in the data and how the information is connected. The Research Topic covers this import topic with three publications:• "C3PO: A Crop Planning and Production Process Ontology and Knowledge Graph" by Baptiste Darnala, Florence Amardeilh, Catherine Roussey, Konstantin Todorov and Clément Jonquet presents the design method to build and update a modular ontology and associated knowledge graph about vegetable production and planification activities. Some new design patterns are defined dedicated to agriculture. For example, the set of planned tasks that compose a technical itinerary of a crop type are presented. The final C3PO knowledge graph was used by the Elzeard enterprise to build three decision information systems. • "CowMesh: A Data-mesh Architecture to Unify Dairy Industry Data for Prediction and Monitoring" by Arjun Pakrashi, Duncan Wallace, Brian Mac Namee, Derek Greene and Christophe Guéret presents an approach to integrate data in the dairy industry by leveraging a combination of data mesh and data fabric design pattern. The approach is presented from a general point of view along with two specific use-case examples for the dairy industry. • "Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case study on grain legumes" by Baptiste Imbert, Jonathan Kreplak, Raphaël-Gauthier Flores, Grégoire Aubert, Judith Burstin and Nadim Tayeh presents the design method of a Neo4J graph database that integrates the trait and gene information extracted from several sources. The graph model reuses existing ontologies like the Gene Ontology (GO), the Plant Ontology (PO) and the Plant Experimental Condition Ontology (PECO). The method was applied on the database design related to five legume species. • "Combining different points of view on plant descriptions: mapping agricultural plant roles and biological taxa" by Florence Amardeilh, Sophie Aubin, Stephan Bernard , Sonia Bravo, Robert Bossy, Catherine Faron, Franck Michel, Juliette Raphel and Catherine Roussey presents some guidelines to publish a mapping dataset between two knowledge graphs: The French Crop Usage thesaurus defined crop usage expressed in French. TAXREF is the nomenclatural and taxonomic repository of living organisms that appear in French territories. A new specialized RDF vocabulary of mapping is defined and presented. • "Integrating collective know-how for multicriteria decision support in agrifood chains: application to cheesemaking" by Patrice Buche, Julien Couteaux, Julien Cufi, Sébastien Destercke and Alrick Oudot presents a multi-criteria decision support system (MDCSS) based on the capture and modelisation of collective know-how in a Knowledge Graph. The ontology for expressing this information is introduced together with an example application for the process of cheese making.Lastly, to illustrate the "Knowledge" part of a KG and reasoning over this knowledge, we have in this issue one paper covering using a KG to infer new information:• "Using knowledge graphs to infer gene expression in plants" by Anne E Thessen, Laurel Cooper, Tyson L Swetnam, Harshad Hegde, Justin Reese, Justin Elser and Pankaj Jaiswal illustrates how a knowledge graph connecting partial information available about different plants can lead to new insights. Leveraging homologous genes as an inference back-end it is possible, as shown, to infer some of the unknown phenotypic impacts of plants gene regulatory networks.We would like to thank the authors who submitted articles, the reviewers who evaluated them and the external editors who managed the reviews. All these people helped build a quality program for this Research Topic.