Constructing TCM Knowledge Graph with Multi-Source Heterogeneous Data

[Objective] This paper constructs a knowledge graph for Traditional Chinese Medicine(TCM) with multi-source heterogeneous data. It supports research innovation in TCM.[Methods] First, we obtained the TCM patents from the IncoPat database. We retrieved the targets and disease data from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) and Online Mendelian Inheritance in Man (OMIM). Then, we extracted the entity and relationship of TMC patents with the deep learning information joint extraction model. We also used string matching and dictionaries to finish the data specification and entity alignment. Third, we constructed the TCM knowledge graph based on the ontology structure we designed. Finally, we analyzed the optimization of TCM prescriptions with the frequency analysis and Apriori algorithm. [Results] The ontology structure designed in this paper contains 31 entity types and 48 semantic relationships, covering specific entities such as solutions and technical effects in TCM patents. We examined the effectiveness of the knowledge graph and the efficiency of optimizing prescriptions with the diabetic nephropathy data. [Limitations] It took us a long time to manually annotate some samples to extract textual information. [Conclusions] The knowledge graph constructed in this paper provides data support for TCM research. It also benefits prescription optimization and realizes multivariate research in TCM. © 2023 Data Analysis and Knowledge Discovery. All rights reserved.