The methodology of mapping relational data to graph data for the construction of knowledge graph in Chinese medical records

Building knowledge graphs based on relational databases has increasingly become a research focus in the field of knowledge engineering. This paper explores the mapping methods for converting relational data into graph data, using the construction of clinical knowledge graphs from electronic medical record (EMR) relational data as a case study. By analyzing the differences in data expression structures between EMR relational data and knowledge graph models, the paper proposes a semantic model for mapping relational data to graph data that meets application semantic requirements, defines rules for converting relational data with varying granularity and semantic constraints into graph structures, and designs a mapping algorithm where the order of mapping is determined by node degree. Experiments demonstrate that the proposed method can effectively implemente knowledge graph construction based on relational data.