梳理食物语义网资源，助力营养数据智能应用
Food Data in the Semantic Web: A Review of Nutritional Resources, Knowledge Graphs, and Emerging Applications
- 整合USDA、FoodOn等主流食物数据源，构建语义关联
- 实现跨源食物实体识别与链接，提升数据一致性
- 适合营养计算、个性化饮食研究者参考
本文系统综述了语义网在食物领域的应用，重点分析USDA、FoodOn、FooDB和Recipe1M+等关键营养数据资源的贡献。聚焦食物实体链接与识别技术，推动异构食物数据的语义融合。探讨食物知识图谱在语义互操作性、数据增强和知识提取中的作用，应用于个性化营养、配料替代、食物-药物/疾病相互作用及跨学科研究。总结现有进展，揭示挑战，为未来利用语义技术发展食物领域提供指引。
原文摘要 · Abstract (English)
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.
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