  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.
