Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908580699963392 |
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| author | Dalal, Aryan Singh Zhang, Yinglun Doğan, Duru İleri, Atalay Mert McGinty, Hande Küçük |
| author_facet | Dalal, Aryan Singh Zhang, Yinglun Doğan, Duru İleri, Atalay Mert McGinty, Hande Küçük |
| contents | The focus on "food as medicine" is gaining traction in the field of health and several studies conducted in the past few years discussed this aspect of food in the literature. However, very little research has been done on representing the relationship between food and health in a standardized, machine-readable format using a semantic web that can help us leverage this knowledge effectively. To address this gap, this study aims to create a knowledge graph to link food and health through the knowledge graph's ability to combine information from various platforms focusing on flavonoid contents of food found in the USDA databases and cancer connections found in the literature. We looked closely at these relationships using KNARM methodology and represented them in machine-operable format. The proposed knowledge graph serves as an example for researchers, enabling them to explore the complex interplay between dietary choices and disease management. Future work for this study involves expanding the scope of the knowledge graph by capturing nuances, adding more related data, and performing inferences on the acquired knowledge to uncover hidden relationships. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06433 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health Dalal, Aryan Singh Zhang, Yinglun Doğan, Duru İleri, Atalay Mert McGinty, Hande Küçük Artificial Intelligence The focus on "food as medicine" is gaining traction in the field of health and several studies conducted in the past few years discussed this aspect of food in the literature. However, very little research has been done on representing the relationship between food and health in a standardized, machine-readable format using a semantic web that can help us leverage this knowledge effectively. To address this gap, this study aims to create a knowledge graph to link food and health through the knowledge graph's ability to combine information from various platforms focusing on flavonoid contents of food found in the USDA databases and cancer connections found in the literature. We looked closely at these relationships using KNARM methodology and represented them in machine-operable format. The proposed knowledge graph serves as an example for researchers, enabling them to explore the complex interplay between dietary choices and disease management. Future work for this study involves expanding the scope of the knowledge graph by capturing nuances, adding more related data, and performing inferences on the acquired knowledge to uncover hidden relationships. |
| title | Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.06433 |