Food Data in the Semantic Web: A Review of Nutritional Resources, Knowledge Graphs, and Emerging Applications
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911132041609216 |
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| author | Sasanski, Darko Stojanov, Riste |
| author_facet | Sasanski, Darko Stojanov, Riste |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00986 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Food Data in the Semantic Web: A Review of Nutritional Resources, Knowledge Graphs, and Emerging Applications Sasanski, Darko Stojanov, Riste Information Retrieval 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. |
| title | Food Data in the Semantic Web: A Review of Nutritional Resources, Knowledge Graphs, and Emerging Applications |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2509.00986 |