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.

翻译：各种网络(例如物的互联网和移动网络)、数据库(例如营养表和食品组成数据库)和社交媒体(例如Instagram和Twitter)的部署产生了大量的粮食数据,为研究人员提供了一个前所未有的机会,通过数据驱动的计算方法研究食品科学和工业的各种问题和应用,然而,这些多种来源的粮食数据似乎是信息筒仓,导致难以充分利用这些粮食数据。知识图表以结构化的形式提供了统一和标准化的概念术语,从而能够有效地组织这些粮食数据,以有利于各种应用。我们在这次审查中简要介绍了知识图表和粮食知识组织的发展,主要从食品知识图到食品知识图。我们然后总结了食品知识图的七个代表性应用,例如新的食谱开发、饮食-不易感相关发现和个人化饮食建议。我们还讨论了该领域的未来方向,例如多式联运食品知识图的构建和食品知识图,以促进人类健康。