Patterns (May 2022)
Applications of knowledge graphs for food science and industry
Abstract
Summary: The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health. The bigger picture: Knowledge graphs can effectively organize data and represent knowledge so that they can be efficiently and extensively explored in traditional and advanced applications in many fields, such as medicine and finance, with no exception of the food domain. The knowledge graph can transform huge amounts of multidisciplinary and heterogeneous food data from various sources to a more reusable globally digitally connected Internet of Food to benefit food science and industry. In this review, we summarize various applications of knowledge graphs that span different aspects of food science and industry. We also discuss future directions in this field, ranging from their construction, representation, reasoning, and applications. We argue that knowledge graphs will enable Internet of Food and food intelligence for their capability in representation and reasoning. Their great potentials will attract more research efforts to apply knowledge graphs in the field of food science and industry.
