The BaiBu Knowledge Engine: A Solution for Improving the Semantic Knowledge Base of Traditional Chinese Medicine

Objective: To update and upgrade the semantic annotation system [1] for Traditional Chinese Medicine(TCM) literatures developed by our team in the previous period, oriented to the actual application requirements. Methods: The workflow and functional modules of the semantic annotation system are updated and upgraded to meet the functional requirements of practical application scenarios, and special functions are developed. Results: Based on the previous system, the BaiBu Knowledge Engine was upgraded and developed with new functions such as setting and visualizing the multi-level structure of entities and semantic relations at the schema layer and event annotation, and we have improved the model, and adding new functions such as semantic search of the system's front-end knowledge base and visualization of the knowledge sources for knowledge traceability, so as to improve the annotation personnel's knowledge and knowledge management skills, the new features include semantic search and visualization of knowledge sources for knowledge traceability in the front-end of the system, in order to improve the annotation efficiency of the annotators and to express the deep implicit knowledge of TCM's literature. Conclusion: The updated and upgraded BaiBu Knowledge Engine has been verified and put into use in actual projects, which can provide powerful support for the expression and deep utilization of the knowledge of TCM literature, and realize data integration and knowledge fusion at the semantic level of TCM.