Construction of multi-modal nutritional knowledge graph
10.3969/j.issn.1673-9701.2025.17.004
- VernacularTitle:多模态营养知识图谱构建
- Author:
Meiling CHE
1
;
Jiale NAN
1
;
Jianhai LIN
1
;
Dongping GAO
1
Author Information
1. 中国医学科学院/北京协和医学院医学信息研究所,北京 100020
- Publication Type:Journal Article
- Keywords:
Multi-modal knowledge graph;
Knowledge representation;
Healthy diet
- From:
China Modern Doctor
2025;63(17):12-15
- CountryChina
- Language:Chinese
-
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.
