Construction of a Traditional Chinese Medicine Dao Yin Science Knowledge Graph Based on Neo4j * .

Construct a Knowledge Graph based on the characteristics of Dao Yin Science in Traditional Chinese Medicine to facilitate users in querying, learning, and disseminating the culture of health maintenance in Traditional Chinese Medicine, and to enhance their health literacy. Using the Bert-CRF model, with manual proofreading, we conducted named entity recognition on literature related to Dao Yin practice, established relationships between entities based on the characteristics of the subject and built a Knowledge Graph for Dao Yin Science using Neo4j. Incorporating 3152 relevant documents, the constructed knowledge graph contains 2262 entity nodes, 5108 relationship data, 7 types of entity attribute categories, and 7 types of relationships. The knowledge graph can be searched using the Cypher query language of Neo4j. This Knowledge Graph visualizes the knowledge of Dao Yin Science in Traditional Chinese Medicine and can be employed to build applications and web platforms for knowledge retrieval, intelligent question answering, and exercise recommendation. It holds practical value for developing related artificial intelligence devices and fostering innovation in the fields of computer science and automation. Moreover, it can be utilized for in-depth data mining and uncovering new research directions.