Flavonoid Fusion: Creating a Knowledge Graph to Unveil the Interplay Between Food and Health

2025-10-07

AI Summary

Food–health knowledge—particularly regarding flavonoid compounds and diseases such as cancer—is fragmented, heterogeneous, and difficult to integrate computationally. Method: We propose and apply the Knowledge-Driven Alignment and Representation Methodology (KNARM) to semantically align and fuse USDA flavonoid composition data with empirically validated cancer-association evidence from the scientific literature. Contribution/Results: This yields the first RDF/OWL-compliant, extensible food–health semantic knowledge graph specifically designed for dietary therapy. The graph enables structured representation of heterogeneous multi-source data, supports SPARQL querying, formal logic reasoning, and cross-entity association mining, thereby establishing the first computable knowledge infrastructure tailored to food-as-medicine research. It facilitates mechanistic computational analysis and inference of dietary interventions, advancing reproducible, evidence-based nutritional science.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Graph-based Machine Learning

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: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Creating a knowledge graph linking food and health relationships
• Standardizing flavonoid-cancer connections in machine-readable format
• Enabling exploration of dietary impacts on disease management

Innovation

• Knowledge graph links food and health data
• KNARM methodology analyzes flavonoid-cancer relationships
• Semantic web integrates USDA and literature sources