摘要
目的为精准、有效、直观地为不同人群提供营养和饮食建议,构建包含食物、营养、人群、疾病等实体的多模态营养知识图谱。方法利用爬虫等技术手段获取营养领域数据集,借鉴OneRel模型完成中文实体关系联合抽取,构建文本库。使用Ro BERTa和ResNet模型分别学习文本和图像数据特征,实现图像与文本的链接,构建多模态知识图谱。结果实体关系联合抽取模型的F1值为0.703,构建的多模态知识图谱中包含3312个文本实体、11259条关系、1000张图像实体。结论本研究构建的多模态营养知识图谱可达到较好的效果,该图谱不仅能系统整合营养领域多模态知识,实现良好可视化查询,也能完成智能问答、营养推荐系统等下游任务的底层支撑。
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,11259 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.
作者
车美龄
南嘉乐
林建海
高东平
CHE Meiling;NAN Jiale;LIN Jianhai;GAO Dongping(Institute of Medical Information,Chinese Academy of Medical Sciences,Peking Union Medical College,Beijing 100020,China)
出处
《中国现代医生》
2025年第17期12-15,共4页
China Modern Doctor
基金
科技创新2030“新一代人工智能”重大专项(2020AAA0104905)。
关键词
多模态知识图谱
知识表达
健康饮食
Multi-modal knowledge graph
Knowledge representation
Healthy diet
