Construction of Depression Knowledge Graph Based on Biomedical Literature

Zepeng Li, Yufeng Zhang, Rikui Huang, Zhenwen Zhang, Jianghong Zhu, Zhihua Guo, Bin Hu

Abstract: Depression is a common mood disorder, which has the characteristics of high prevalence, high recurrence rate, high disability rate and high mortality rate. There are a large number of medical literature on depression, but the number is large and disorderly, which will undoubtedly increase the burden of biomedical researchers and medical workers to obtain knowledge, and is not conducive to the research on the pathogenesis and treatment of depression. Therefore, we construct a knowledge graph of depression based on biomedical literature to assist the study of depression. We use medical abstracts as the main data source and extract knowledge from them by using SemRep, which is a biomedical information extraction system. Secondly, we use another information extraction tool named OpenIE to correct the data extracted by SemRep. Then, by fusing the extracted knowledge with structured data extracted from SemMedDB, we finally get 8,840 triples which include 3,055 entities and 30 relationships. We store them into the graph database Neo4j to visualize the knowledge graph.