TY - GEN
T1 - Construction of Depression Knowledge Graph Based on Biomedical Literature
AU - Li, Zepeng
AU - Zhang, Yufeng
AU - Huang, Rikui
AU - Zhang, Zhenwen
AU - Zhu, Jianghong
AU - Guo, Zhihua
AU - Hu, Bin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Biomedical Literature
KW - Depression
KW - Knowledge
KW - Knowledge Graph
UR - http://www.scopus.com/inward/record.url?scp=85125205183&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669447
DO - 10.1109/BIBM52615.2021.9669447
M3 - Conference contribution
AN - SCOPUS:85125205183
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 1849
EP - 1855
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -