KG4NH: a comprehensive knowledge graph for question answering in dietary nutrition and human health
Published in IEEE Journal of Biomedical and Health Informatics, 2023
This study constructs a comprehensive knowledge graph on nutrition and human health by extracting triples from vast literature sources. A query-based question-answering system is developed to address three types of queries over this graph. The proposed model outperforms state-of-the-art methods in nutrition-disease relation extraction, achieving a precision of 0.92, recall of 0.81, and an F1 score of 0.86. The question-answering system attains an accuracy of 0.68 and an F1 score of 0.61. Five experiments validate the knowledge graph’s data structure, demonstrating its potential for diet recommendations, patient care, and clinical decision-making.
Recommended citation: Fu, C., Pan, X., Wu, J., Cai, J., Huang, Z., van Harmelen, F., ... & He, T. (2023). KG4NH: a comprehensive knowledge graph for question answering in dietary nutrition and human health. IEEE journal of biomedical and health informatics.
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