Construction of Depression Knowledge Graph Based on Biomedical Literature

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

doi:10.1109/BIBM52615.2021.9669447

Construction of Depression Knowledge Graph Based on Biomedical Literature

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

^*Corresponding author for this work

School of Medical and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1849-1855
Number of pages	7
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669447
Publication status	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: 9 Dec 2021 → 12 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	9/12/21 → 12/12/21

Keywords

Biomedical Literature
Depression
Knowledge
Knowledge Graph

Access to Document

10.1109/BIBM52615.2021.9669447

Cite this

Li, Z., Zhang, Y., Huang, R., Zhang, Z., Zhu, J., Guo, Z., & Hu, B. (2021). Construction of Depression Knowledge Graph Based on Biomedical Literature. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 1849-1855). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669447

Li, Zepeng ; Zhang, Yufeng ; Huang, Rikui et al. / Construction of Depression Knowledge Graph Based on Biomedical Literature. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1849-1855 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{e79fe58df465427d96cce5dff5a5c817,

title = "Construction of Depression Knowledge Graph Based on Biomedical Literature",

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.",

keywords = "Biomedical Literature, Depression, Knowledge, Knowledge Graph",

author = "Zepeng Li and Yufeng Zhang and Rikui Huang and Zhenwen Zhang and Jianghong Zhu and Zhihua Guo and Bin Hu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669447",

language = "English",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1849--1855",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, {Xiaohua Tony} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

address = "United States",

}

Li, Z, Zhang, Y, Huang, R, Zhang, Z, Zhu, J, Guo, Z & Hu, B 2021, Construction of Depression Knowledge Graph Based on Biomedical Literature. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1849-1855, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 9/12/21. https://doi.org/10.1109/BIBM52615.2021.9669447

Construction of Depression Knowledge Graph Based on Biomedical Literature. / Li, Zepeng; Zhang, Yufeng; Huang, Rikui et al.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1849-1855 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 -

Li Z, Zhang Y, Huang R, Zhang Z, Zhu J, Guo Z et al. Construction of Depression Knowledge Graph Based on Biomedical Literature. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1849-1855. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669447

Construction of Depression Knowledge Graph Based on Biomedical Literature

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