中国现代医生2025，Vol.63Issue(17)：12-15,4.DOI:10.3969/j.issn.1673-9701.2025.17.004
多模态营养知识图谱构建
Construction of multi-modal nutritional knowledge graph
摘要
Abstract
Objective To provide precise,effective,and intuitive nutritional and dietary recommendations for different population groups,a multi-modal nutritional knowledge graph was constructed,which includes entities such as food,nutrition,population,and diseases.Methods Data sets in the field of nutrition were obtained using web crawling and other technical means.The OneRel model was referenced to complete the joint extraction of Chinese entity relationships and construct a text library.The RoBERTa-ResNet model were used to learn the features of text and image data separately,to align images with text,and to construct a multi-modal knowledge graph.Results The F1 value of the joint entity relationship extraction model was 0.703.The constructed multi-modal knowledge graph contains 3312 textual entities,11 259 relationships,and 1000 image entities.Conclusion The algorithms used in this study to construct the multi-modal nutritional knowledge graph achieve good results.This knowledge graph not only systematically integrates multi-modal knowledge in the field of nutrition and enables good visual query capabilities,but also serves as the underlying support for downstream tasks such as intelligent question answering and nutritional recommendation systems.关键词
多模态知识图谱/知识表达/健康饮食Key words
Multi-modal knowledge graph/Knowledge representation/Healthy diet分类
医药卫生引用本文复制引用
车美龄,南嘉乐,林建海,高东平..多模态营养知识图谱构建[J].中国现代医生,2025,63(17):12-15,4.基金项目
科技创新2030"新一代人工智能"重大专项(2020AAA0104905) （2020AAA0104905）
