A Knowledge Graph (KG) is based on a graph model to encode the description of entities. As defined by A. Hogan and his collaborators in 2022, a knowledge graph is “a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities.” For Knowledge Graph using Semantic Web technologies, entities (people, events, concepts, etc.) are identified by a Uniform Resource Identifier (URI). This URI is the source of a graph description, the edge specifies the nature of the link (person name or brotherhood relationship) and the destination of the edge could be a simple literal (the person name) or a URI that identifies another entity (the URI of the brother). The main advantage of these technologies is to link entities that are described differently in several knowledge graphs provided by various organizations. Thus, computer scientists may analyze all those graph descriptions to derive new information (detect incoherencies, complete data, etc.).
During the last decade, considerable progress has been made in the construction and enrichment of KGs, including ontology matching, data integration, fact prediction, and validation. This happened largely thanks to the use of techniques developed in the fields of knowledge representation, reasoning, and machine learning. With these advances, more and more applications are now able to produce and process KGs in domains such as life sciences, Galleries/Libraries/Archives/Museums (GLAMs), and health care. The subjects of interest within the Food, Agriculture, and Water domains are often complex phenomena where entities evolve through time and space. Those phenomena may be transformed by different processes and influenced by both human and natural systems. The scientific disciplines that study these phenomena are diverse and do not necessarily share the same vocabularies, the same techniques of observation, the same analyses, and so on. Indeed, each discipline often has its own point of view to describe the complexity of the studied phenomena. KG technologies provide one possible approach to express this diversity of representations and align or combine them.
In this Research Topic, we seek contributions describing methods and use-cases that rely on the application of reasoning and machine learning on knowledge graphs in the Food, Agriculture, and Water domain. We solicit submission articles on substantial, original, and unpublished research in all aspects related to the mentioned issues, dedicated to the Food, Agriculture, and Water domain including but not limited to the following areas:
• ontology and semantic resources engineering,
• data integration and consolidation using graph technologies,
• graph database and repository,
• graph enrichment process and validation,
• best practices in graph data publication (FAIR data),
• graph machine learning,
• hybridization between symbolic reasoning and machine learning applied to graphs,
• complex query answering on graphs,
• reasoning (e.g., spatial, temporal) applied to graph data,
• social, privacy, and ethical consideration for the creation of graphs,
• IoT data stream management and graph reasoning.
A Knowledge Graph (KG) is based on a graph model to encode the description of entities. As defined by A. Hogan and his collaborators in 2022, a knowledge graph is “a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities.” For Knowledge Graph using Semantic Web technologies, entities (people, events, concepts, etc.) are identified by a Uniform Resource Identifier (URI). This URI is the source of a graph description, the edge specifies the nature of the link (person name or brotherhood relationship) and the destination of the edge could be a simple literal (the person name) or a URI that identifies another entity (the URI of the brother). The main advantage of these technologies is to link entities that are described differently in several knowledge graphs provided by various organizations. Thus, computer scientists may analyze all those graph descriptions to derive new information (detect incoherencies, complete data, etc.).
During the last decade, considerable progress has been made in the construction and enrichment of KGs, including ontology matching, data integration, fact prediction, and validation. This happened largely thanks to the use of techniques developed in the fields of knowledge representation, reasoning, and machine learning. With these advances, more and more applications are now able to produce and process KGs in domains such as life sciences, Galleries/Libraries/Archives/Museums (GLAMs), and health care. The subjects of interest within the Food, Agriculture, and Water domains are often complex phenomena where entities evolve through time and space. Those phenomena may be transformed by different processes and influenced by both human and natural systems. The scientific disciplines that study these phenomena are diverse and do not necessarily share the same vocabularies, the same techniques of observation, the same analyses, and so on. Indeed, each discipline often has its own point of view to describe the complexity of the studied phenomena. KG technologies provide one possible approach to express this diversity of representations and align or combine them.
In this Research Topic, we seek contributions describing methods and use-cases that rely on the application of reasoning and machine learning on knowledge graphs in the Food, Agriculture, and Water domain. We solicit submission articles on substantial, original, and unpublished research in all aspects related to the mentioned issues, dedicated to the Food, Agriculture, and Water domain including but not limited to the following areas:
• ontology and semantic resources engineering,
• data integration and consolidation using graph technologies,
• graph database and repository,
• graph enrichment process and validation,
• best practices in graph data publication (FAIR data),
• graph machine learning,
• hybridization between symbolic reasoning and machine learning applied to graphs,
• complex query answering on graphs,
• reasoning (e.g., spatial, temporal) applied to graph data,
• social, privacy, and ethical consideration for the creation of graphs,
• IoT data stream management and graph reasoning.