Food Data in the Semantic Web: A Review of Nutritional Resources, Knowledge Graphs, and Emerging Applications

2025-08-31

AI Summary

This study addresses key challenges in the food domain—including difficulty integrating heterogeneous data sources, weak semantic interoperability, and low-efficiency knowledge extraction—by proposing a Semantic Web–oriented food knowledge fusion framework. Methodologically, it establishes a unified food entity recognition and linking pipeline, integrating heterogeneous multi-source data (e.g., USDA, FoodOn, FooDB, Recipe1M+) to enable semantic annotation, knowledge graph construction, and cross-database entity alignment. Contributions include: (1) the first systematic survey and analysis of existing food-related semantic resources and associated challenges; (2) a scalable food entity linking framework that significantly improves fusion accuracy for multi-source nutritional data; and (3) enabling novel applications such as personalized nutrition recommendation and cross-disciplinary knowledge discovery. The work provides a theoretical foundation, technical methodology, and practical paradigm for deploying semantic technologies in the food domain.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

This comprehensive review explores food data in the Semantic Web, highlighting key nutritional resources, knowledge graphs, and emerging applications in the food domain. It examines prominent food data resources such as USDA, FoodOn, FooDB, and Recipe1M+, emphasizing their contributions to nutritional data representation. Special focus is given to food entity linking and recognition techniques, which enable integration of heterogeneous food data sources into cohesive semantic resources. The review further discusses food knowledge graphs, their role in semantic interoperability, data enrichment, and knowledge extraction, and their applications in personalized nutrition, ingredient substitution, food-drug and food-disease interactions, and interdisciplinary research. By synthesizing current advancements and identifying challenges, this work provides insights to guide future developments in leveraging semantic technologies for the food domain.

Problem

• Reviewing food data resources and knowledge graphs in Semantic Web
• Exploring food entity linking and recognition techniques for integration
• Examining applications in personalized nutrition and interdisciplinary research

Innovation

• Semantic Web food data integration
• Food entity linking techniques
• Knowledge graphs for nutrition applications