Knowledge Graph Completion Based on Contrastive Learning for Diet Therapy

In recent years, an increasing number of individuals have turned to traditional Chinese medicine diet therapy as a means to nourish their bodies and mitigate diseases. With the advent of the big data era, knowledge graphs, as powerful analysis tools, can provide more accurate and personalized dietary advice for diet therapy. However, most of the current diet therapy knowledge graphs have imperfections. To address this issue, we construct a diet therapy knowledge graph by utilizing textual data and professional books provided by the Academy of Traditional Chinese Medicine, from which we extract entities and relations. Building upon this foundation, we introduce a text representation technique predicated on contrastive learning, designed to augment the semantic richness of the knowledge graph and enhance the completion of the diet therapy knowledge graph. By conducting experiments on the diet therapy knowledge graph and public datasets, the results show that our method can capture the semantic information in the knowledge graph more efficiently compared to traditional methods. This provides new possibilities for research and practice in the field of traditional Chinese medicine diet therapy. This research opens new avenues for leveraging big data analysis in traditional Chinese medicine diet therapy.