Applications of knowledge graphs for food science and industry

TL;DR

This paper reviews how knowledge graphs organize diverse food data to enable applications like recipe development and health recommendations, highlighting recent advances and future directions in food science.

Contribution

It provides a comprehensive overview of food knowledge graphs, their evolution, applications, and future research directions in food science and industry.

Findings

Food knowledge graphs unify heterogeneous data sources.

Applications include recipe creation, diet-disease analysis, personalized nutrition.

Future trends involve multimodal data integration and health-focused graphs.

Abstract

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.