构建食物与健康关系的知识图谱，揭示类黄酮的抗癌作用
Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health
- 基于类黄酮数据与癌症研究，用语义网整合多源信息
- 通过KNARM方法建模，实现食物-健康关系的机器可读表达
- 为饮食与疾病管理研究提供可扩展的分析框架
近年来，'食物即药物'的理念在健康领域日益受到关注，已有研究探讨了食物的这一特性。然而，针对食物与健康关系在标准化、机器可读格式下的表征研究仍十分有限。为填补这一空白，本研究旨在构建一个知识图谱，利用其整合来自美国农业部数据库中食物类黄酮含量数据及文献中的癌症关联信息的能力，系统揭示食物与健康之间的复杂互动。研究采用KNARM方法深入分析这些关系，并以机器可操作格式呈现。该知识图谱为研究人员探索饮食选择与疾病管理之间的关联提供了范例。未来工作将扩展知识图谱范围，捕捉更多细节，引入新数据并进行推理，以发现潜在的隐藏关系。
原文摘要 · Abstract (English)
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.
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