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KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human Health
IEEE Journal of Biomedical and Health Informatics
|December 1, 2023
Summary
This study introduces a novel knowledge graph for nutrition and human health, improving information extraction and question answering. The developed system demonstrates superior performance in understanding diet-disease relationships for practical applications.
Area of Science:
- Biomedical Informatics
- Nutritional Science
- Computational Biology
Background:
- Food nutrition is intrinsically linked to human health.
- Complex interactions between nutrients, diseases, and gut microbiota hinder knowledge application.
- Systematizing and applying nutritional knowledge requires advanced methods.
Purpose of the Study:
- To develop a method for extracting nutrition-related information from literature.
- To construct a comprehensive knowledge graph on nutrition and human health.
- To create a question answering system for querying the knowledge graph.
Main Methods:
- Triple extraction from scientific literature.
- Knowledge graph construction.
- Development of a query-based question answering system.
- Evaluation of relation extraction and question answering performance.
Main Results:
- The relation extraction model achieved 0.92 precision, 0.81 recall, and 0.86 F1 score.
- The question answering system achieved 0.68 accuracy and 0.61 F1 score.
- Knowledge graph data structure quality was validated through five experiments.
Conclusions:
- The constructed knowledge graph effectively integrates nutrition and health information.
- The system shows significant potential for personalized diet recommendations.
- Applications include enhanced patient care and informed clinical research decisions.
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