From DIKW pyramid to graph database: a tool for machine processing of nutritional epidemiologic research data

There is an increased interest in the application of information technology to advance nutritional research. In nutrition science, a graph database enables the creation of multilateral logic relationships throughout the database, which can be used to electronically store, visualize, and scale the outputs of nutritional research. It provides a knowledge structure to standardize nutritional research outputs, which is both human- and machine-readable in a Resource Description Framework format. However, the development of various specific graph databases may cause difficulties for data integration and decrease human-readability. In this article, we propose an approach to develop a graph database according to the Data, Information, Knowledge, and Wisdom or “DIKW” pyramid for nutritional epidemiologic data. Then, authoritative ontologies are suggested to construct the nodes and edges of the graph database to facilitate data integration. Finally, the findability and re-usability of the knowledge in the graph database are showcased using the SPARQL and SQWRL query languages.