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Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.


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Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Show Author's information Xuehong Wu1,2,Junwen Duan1,Yi Pan3Min Li1( )
School of Computer Science and Engineering, Central South University, Changsha 410083, China
School of Computer Science, Hunan First Normal University, Changsha 410006, China
Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China

Xuehong Wu and Junwen Duan contribute equally to this work.

Abstract

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Keywords: medical knowledge graph, knowledge graph construction, knowledge reasoning, intelligent medical applications, intelligent healthcare

References(145)

[1]
A. Singhal, Introducing the knowledge graph: Things, not strings, https://blog.google/products/search/introducing-knowledge-graph-things-not/, 2012.
[2]
J. Wang, X. Wang, C. Ma, and L. Kou, A survey on the development status and application prospects of knowledge graph in smart grids, IET Gener. Transm. Distrib., vol. 15, no. 3, pp. 383–407, 2021.
[3]
E. I. Papageorgiou, C. Huszka, J. De Roo, N. Douali, M. C. Jaulent, and D. Colaert, Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support, Comput. Methods Programs Biomed., vol. 112, no. 3, pp. 580–598, 2013.
[4]
T. Yu, J. Li, Q. Yu, Y. Tian, X. Shun, L. Xu, L. Zhu, and H. Gao, Knowledge graph for TCM health preservation: Design, construction, and applications, Artif. Intell. Med., vol. 77, pp. 48–52, 2017.
[5]
P. Ping, K. Watson, J. Han, and A. Bui, Individualized knowledge graph: A viable informatics path to precision medicine, Circ. Res., vol. 120, no. 7, pp. 1078–1080, 2017.
[6]
D. M. Bean, H. Wu, E. Iqbal, O. Dzahini, Z. M. Ibrahim, M. Broadbent, R. Stewart, and R. J. B. Dobson, Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records, Sci. Rep., vol. 7, no. 1, p. 16416, 2017.
[7]
G. Bakal, P. Talari, E. V. Kakani, and R. Kavuluru, Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations, J. Biomed. Inform., vol. 82, pp. 189–199, 2018.
[8]
Y. Dai, C. Guo, W. Guo, and C. Eickhoff, Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings, Brief. Bioinform., vol. 22, no. 4, p. bbaa256, 2020.
[9]
H. Xu, J. Li, L. Nie, and X. Xu, CKGECS: A Chinese knowledge graph for elderly care service, Int.J. Serv. Technol. Manag., vol. 26, nos. 2&3, pp. 131–148, 2020.
[10]
C. Chen, K. E. Ross, S. Gavali, J. E. Cowart, and C. H. Wu, COVID-19 knowledge graph from semantic integration of biomedical literature and databases, Bioinformatics, vol. 37, no. 23, pp. 4597–4598, 2021.
[11]
Q. Zhao, J. Li, C. Xu, J. Yang, and L. Zhao, Knowledge-enhanced relation extraction for Chinese EMRs, IT Prof., vol. 22, no. 4, pp. 57–62, 2020.
[12]
T. D. Gunter and N. P. Terry, The emergence of national electronic health record architectures in the United States and Australia: Models, costs, and questions, J. Med. Internet Res., vol. 7, no. 1, p. e3, 2005.
[13]
Z. Huang, Q. Hu, M. Liao, C. Miao, C. Wang, and G. Liu, Knowledge graphs of Kawasaki disease, Health Inf. Sci. Syst., vol. 9, no. 1, p. 11, 2021.
[14]
X. Tao, T. Pham, J. Zhang, J. Yong, W. P. Goh, W. Zhang, and Y. Cai, Mining health knowledge graph for health risk prediction, World Wide Web, vol. 23, no. 4, pp. 2341–2362, 2020.
[15]
S. M. S. Hasan, D. Rivera, X. C. Wu, E. B. Durbin, J. B. Christian, and G. Tourassi, Knowledge graph-enabled cancer data analytics, IEEE J. Biomed. Health Inform., vol. 24, no. 7, pp. 1952–1967, 2020.
[16]
C. E. Lipscomb, Medical Subject Headings (MeSH), Bull. Med. Libr. Assoc., vol. 88, no. 3, pp. 265–266, 2000.
[17]
K. Donnelly, SNOMED-CT: The advanced terminology and coding system for eHealth, Stud. Health Technol. Inform., vol. 121, pp. 279–290, 2006.
[18]
O. Bodenreider, The Unified Medical Language System (UMLS): Integrating biomedical terminology, Nucl. Acids Res., vol. 32, no. S1, pp. D267-D270, 2004.
[19]
D. S. Wishart, C. Knox, A. C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang, and J. Woolsey, DrugBank: A comprehensive resource for in silico drug discovery and exploration, Nucl. Acids Res., vol. 34, pp. D668–D672, 2006.
[20]
M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato, and K. Morishima, KEGG: New perspectives on genomes, pathways, diseases and drugs, Nucl. Acids Res., vol. 45, no. D1, pp. D353–D361, 2017.
[21]
J. Xie, J. Jiang, Y. Wang, Y. Guan, and X. Guo, Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis, Artif. Intell. Med., vol. 107, p. 101927, 2020.
[22]
Y. Zhang, M. Sheng, R. Zhou, Y. Wang, G. Han, H. Zhang, C. Xing, and J. Dong, HKGB: An inclusive, extensible, intelligent, semi-auto-constructed knowledge graph framework for healthcare with clinicians’ expertise incorporated, Inform. Proc. Manag., vol. 57, no. 6, p. 1023246, 2020.
[23]
K. M. Malik, M. Krishnamurthy, M. Alobaidi, M. Hussain, F. Alam, and G. Malik, Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype, Expert Syst. Appl., vol. 145, p. 113120, 2020.
[24]
M. A. Musen, The protégé project: A look back and a look forward, AI Matt., vol. 1, no. 4, pp. 4–12, 2015.
[25]
M. Alobaidi, K. M. Malik, and M. Hussain, Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain, Comput. Methods Programs Biomed., vol. 165, pp. 117–128, 2018.
[26]
S. Feng, H. Ning, S. Yang, and D. Zhao, Geriatric disease reasoning based on knowledge graph, in Int. 2019 Cyberspace Congress, CyberDI and CyberLife on Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health, Beijing, China, 2019, pp. 452–465.
[27]
D. Korn, T. Bobrowski, M. Li, Y. Kebede, P. Wang, P. Owen, G. Vaidya, E. Muratov, R. Chirkova, C. Bizon, et al., COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database, Bioinformatics, vol. 37, no. 4, pp. 586–587, 2021.
[28]
F. Zhang, B. Sun, X. Diao, W. Zhao, and T. Shu, Prediction of adverse drug reactions based on knowledge graph embedding, BMC Med. Inform. Decis. Mak., vol. 21, no. 1, p. 38, 2021.
[29]
K. Zhang, K. Li, H. Ma, D. Yue, and L. Zhuang, Construction of MeSH-like obstetric knowledge graph, in Proc. 2018 Int. Conf. Cyber-Enabled Distributed Computing and Knowledge Discovery, New York, NY, USA, 2018, pp. 160–168.
[30]
C. Zhao, J. Jiang, Y. Guan, X. Guo, and B. He, EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning, Artif. Intell. Med., vol. 87, pp. 49–59, 2018.
[31]
M. R. Kamdar, C. E. Stanley Jr, M. Carroll, L. Wogulis, W. Dowling, H. F. Deus, and M. Samarasinghe, Text snippets to corroborate medical relations: An unsupervised approach using a knowledge graph and embeddings, AMIA Jt. Summits Transl. Sci. Proc., vol. 2020, pp. 288–297, 2020.
[32]
F. Michel, F. Gandon, V. Ah-Kane, A. Bobasheva, E. Cabrio, O. Corby, R. Gazzotti, A. Giboin, S. Marro, T. Mayer, et al., Covid-on-the-Web: Knowledge graph and services to advance COVID-19 research, in 19th Int. Semantic Web Conf. on the Semantic Web, Athens, Greece, 2020, pp. 294–310.
[33]
P. Liu, X. Wang, X. Sun, X. Shen, X. Chen, Y. Sun, and Y. Pan, HKDP: A hybrid knowledge graph based pediatric disease prediction system, in Int. Conf. on Smart Health, Haikou, China, 2017, pp. 78–90.
[34]
X. Li, Y. Wang, D. Wang, W. Yuan, D. Peng, and Q. Mei, Improving rare disease classification using imperfect knowledge graph, BMC Med. Inform. Decis. Mak., vol. 19, no. 5, p. 238, 2019.
[35]
A. Ettorre, O. R. Rodríguez, C. Faron, F. Michel, and F. Gandon, A knowledge graph enhanced learner model to predict outcomes to questions in the medical field, in 22n⁢d Int. Conf. on Knowledge Engineering and Knowledge Management, Bolzano, Italy, 2020, pp. 237–251.
[36]
S. Ansong, K. F. Eteffa, C. Li, M. Sheng, Y. Zhang, and C. Xing, How to empower disease diagnosis in a medical education system using knowledge graph, in 16th Int. Conf. on Web Information Systems and Applications, Qingdao, China, 2019, pp. 518–523.
[37]
M. Wang, X. Ma, J. Si, H. Tang, H. Wang, T. Li, W. Ouyang, L. Gong, Y. Tang, and X. He, et al., Adverse drug reaction discovery using a tumor-biomarker knowledge graph, Front. Genet., vol. 11, p. 625659, 2021.
[38]
I. Abdelaziz, A. Fokoue, O. Hassanzadeh, P. Zhang, and M. Sadoghi, Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions, J. Web Semant., vol. 44, pp. 104–117, 2017.
[39]
M. Sheng, Q. Hu, Y. Zhang, C. Xing, and T. Zhang, A data-intensive CDSS platform based on knowledge graph, in 7th Int. Conf. on Health Information Science, Cairns, Australia, 2018, pp. 146–155.
[40]
F. Song, B. Wang, Y. Tang, and J. Sun, Research of medical aided diagnosis system based on temporal knowledge graph, in 16t⁢h Int. Conf. on Advanced Data Mining and Applications, Foshan, China, 2020, pp. 236–250.
[41]
Q. Bao, L. Ni, and J. Liu, HHH: An online medical chatbot system based on knowledge graph and hierarchical bi-directional attention, in Proc. Australasian Computer Science Week Multiconference, Melbourne, Australia, 2020, pp. 1–10.
[42]
Z. Jiang, C. Chi, and Y. Zhan, Research on medical question answering system based on knowledge graph, IEEE Access, vol. 9, pp. 21094–21101, 2021.
[43]
Y. Duan, P. Ji, L. Jin, A. Zou, J. Yang, H. Xie, and N. An, A knowledge graph for eldercare: Constructing a domain entity graph with guidelines, in 4th Int. Conf. on Human Aspects of IT for the Aged Population. Applications in Health, Assistance, and Entertainment, Vegas, NV, USA, 2018, pp. 25–35.
[44]
X. He, R. Zhang, R. Rizvi, J. Vasilakes, X. Yang, Y. Guo, Z. He, M. Prosperi, J. Huo, J. Alpert, et al., ALOHA: Developing an interactive graph-based visualization for dietary supplement knowledge graph through user-centered design, BMC Med. Inform. Decis. Mak., vol. 19, no. 4, p. 150, 2019.
[45]
H. Sun, J. Xiao, W. Zhu, Y. He, S. Zhang, X. Xu, L. Hou, J. Li, Y. Ni, and G. Xie, Medical knowledge graph to enhance fraud, waste, and abuse detection on claim data: Model development and performance evaluation, JMIR Med. Inform., vol. 8, no. 7, p. e17653, 2020.
[46]
T. Pham, X. Tao, J. Zhang, and J. Yong, Constructing a knowledge-based heterogeneous information graph for medical health status classification, Health Inf. Sci. Syst., vol. 8, no. 1, p. 10, 2020.
[47]
L. Li, P. Wang, J. Yan, Y. Wang, S. Li, J. Jiang, Z. Sun, B. Tang, T. H. Chang, S. Wang, et al., Real-world data medical knowledge graph: Construction and applications, Artif. Intell. Med., vol. 103, p. 101817, 2020.
[48]
M. Wang, J. Zhang, J. Liu, W. Hu, S. Wang, X. Li, and W. Liu, PDD Graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking, in 16th Int. Semantic Web Conf. on the Semantic Web, Vienna, Austria, 2017, pp. 219–227.
[49]
X. Zhang and C. Che, Drug repurposing for parkinson’s disease by integrating knowledge graph completion model and knowledge fusion of medical literature, Future Internet, vol. 13, no. 1, p. 14, 2021.
[50]
P. Ernst, A. Siu, and G. Weikum, KnowLife: A versatile approach for constructing a large knowledge graph for biomedical sciences, BMC Bioinform., vol. 16, no. 1, p. 157, 2015.
[51]
M. A. Patil, S. Bhaumik, S. Paul, S. Bissoyi, R. Roy, and S. Ryu, Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS), in 2013 35th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 2013, pp. 1274–1277.
[52]
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, Freebase: A collaboratively created graph database for structuring human knowledge, in Proc. 2008 ACM SIGMOD Int. Conf. Management of Data, Vancouver, Canada, 2008, pp. 1247–1250.
[53]
A. S. Brown and C. J. Patel, A standard database for drug repositioning, Sci. Data, vol. 4, no. 1, p. 170029, 2017.
[54]
H. Kilicoglu, D. Shin, M. Fiszman, G. Rosemblat, and T. C. Rindflesch, SemMedDB: A PubMed-scale repository of biomedical semantic predications, Bioinformatics, vol. 28, no. 23, pp. 3158–3160, 2012.
[55]
F. Teng, W. Yang, L. Chen, L. Huang, and Q. Xu, Explainable prediction of medical codes with knowledge graphs, Front. Bioeng. Biotechnol., vol. 8, p. 867, 2020.
[56]
T. B. Malas, W. J. Vlietstra, R. Kudrin, S. Starikov, M. Charrout, M. Roos, D. J. M. Peters, J. A. Kors, R. Vos, P. A. C. ‘t Hoen, et al., Drug prioritization using the semantic properties of a knowledge graph, Sci. Rep., vol. 9, no. 1, p. 6281, 2019.
[57]
C. Bizon, S. Cox, J. Balhoff, Y. Kebede, P. Wang, K. Morton, K. Fecho, and A. Tropsha, ROBOKOP KG and KGB: Integrated knowledge graphs from federated sources, J. Chem. Inf. Model., vol. 59, no. 12, pp. 4968–4973, 2019.
[58]
T. R. Goodwin and S. M. Harabagiu, Medical question answering for clinical decision support, Proc. ACM Int. Conf. Inf. Knowl. Manag., vol. 2016, pp. 297–306, 2016.
[59]
M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng, and D. Sontag, Learning a health knowledge graph from electronic medical records, Sci. Rep., vol. 7, no. 1, p. 5994, 2017.
[60]
B. Cheng, Y. Zhang, D. Cai, W. Qiu, and D. Shi, Construction of traditional Chinese medicine knowledge graph using data mining and expert knowledge, in Proc. 2018 IEEE Int. Conf. Network Infrastructure and Digital Content, Guiyang, China, 2018, pp. 209–213.
[61]
M. Sheng, Y. Shao, Y. Zhang, C. Li, C. Xing, H. Zhang, J. Wang, and F. Gao, DEKGB: An extensible framework for health knowledge graph, in International Conference on Smart Health, Shenzhen, China, 2019, pp. 27–38.
[62]
L. Li, P. Wang, Y. Wang, S. Wang, J. Yan, J. Jiang, B. Tang, C. Wang, and Y. Liu, A method to learn embedding of a probabilistic medical knowledge graph: Algorithm development, JMIR Med. Inform., vol. 8, no. 5, p. e17645, 2020.
[63]
L. Wang, H. Xie, W. Han, X. Yang, L. Shi, J. Dong, K. Jiang, and H. Wu, Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences, Comput. Assist. Surg., vol. 25, no. 1, pp. 29–35, 2020.
[64]
M. Sheng, A. Li, Y. Bu, J. Dong, Y. Zhang, X. Li, C. Li, and C. Xing, DSQA: A domain specific QA system for smart health based on knowledge graph, in 17th Int. Conf. on Web Information Systems and Applications, Guangzhou, China, 2020, pp. 215–222.
[65]
X. Li, H. Liu, X. Zhao, G. Zhang, and C. Xing, Automatic approach for constructing a knowledge graph of knee osteoarthritis in Chinese, Health Inf. Sci. Syst., vol. 8, no. 1, p. 12, 2020.
[66]
F. Gong, M. Wang, H. Wang, S. Wang, and M. Liu, SMR: Medical knowledge graph embedding for safe medicine recommendation, Big Data Res., vol. 23, p. 100174, 2021.
[67]
Z. Lin, D. Yang, and X. Yin, Patient similarity via joint embeddings of medical knowledge graph and medical entity descriptions, IEEE Access, vol. 8, pp. 156663–156676, 2020.
[68]
W. Deng, P. Guo, and J. Yang, Medical entity extraction and knowledge graph construction, in 2019 16th Int. Computer Conf. Wavelet Active Media Technology and Information Processing, Chengdu, China, 2019, pp. 41–44.
[69]
T. Ruan, Y. Huang, X. Liu, Y. Xia, and J. Gao, QAnalysis: A question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research, BMC Med. Inform. Decis. Mak., vol. 19, no. 1, p. 82, 2019.
[70]
W. Van Woensel, C. Armstrong, M. Rajaratnam, V. Gupta, and S. S. R. Abidi, Using knowledge graphs to plausibly infer missing associations in EMR data, Stud. Health Technol. Inform., vol. 281, pp. 417–421, 2021.
[71]
A. Fang, P. Lou, J. Hu, W. Zhao, M. Feng, H. Ren, and X. Chen, Head and tail entity fusion model in medical knowledge graph construction: Case study for pituitary adenoma, JMIR Med. Inform., vol. 9, no. 7, p. e28218, 2021.
[72]
A. K. Talukder, J. B. Sanz, and J. Samajpati, ‘Precision Health’: Balancing reactive care and proactive care through the evidence based knowledge graph constructed from real-world electronic health records, disease trajectories, diseasome, and patholome, in 8th Int. Conf. on Big Data Analytics, Sonepat, India, 2020, pp. 113–133.
[73]
I. Y. Chen, M. Agrawal, S. Horng, and D. Sontag, Robustly extracting medical knowledge from EHRs: A case study of learning a health knowledge graph, Pac. Symp. Biocomput., vol. 25, pp. 19–30, 2020.
[74]
L. Shi, S. Li, X. Yang, J. Qi, G. Pan, and B. Zhou, Semantic health knowledge graph: Semantic integration of heterogeneous medical knowledge and services, Biomed. Res. Int., vol. 2017, p. 2858423, 2017.
[75]
J. Lee, C. Liu, J. H. Kim, A. Butler, N. Shang, C. Pang, K. Natarajan, P. Ryan, C. Ta, and C. Weng, Comparative effectiveness of medical concept embedding for feature engineering in phenotyping, JAMIA Open, vol. 4, no. 2, p. ooab028, 2021.
[76]
K. He, L. Yao, J. Zhang, Y. Li, and C. Li, Construction of genealogical knowledge graphs from obituaries: Multitask neural network extraction system, J. Med. Internet. Res., vol. 23, no. 8, p. e25670, 2021.
[77]
H. Weng, Z. Liu, S. Yan, M. Fan, A. Ou, D. Chen, and T. Hao, A framework for automated knowledge graph construction towards traditional Chinese medicine, in 6th Int. Conf. on Health Information Science, Moscow, Russia, 2017, pp. 170–181.
[78]
J. Du and X. Li, A knowledge graph of combined drug therapies using semantic predications from biomedical literature: Algorithm development, JMIR Med. Inform., vol. 8, no. 4, p. e18323, 2020.
[79]
Z. Huang, J. Yang, F. van Harmelen, and Q. Hu, Constructing knowledge graphs of depression, in lecture notes in computer science, in 6th Int. Conf. on Health Information Science, Moscow, Russia, 2017, pp. 149–161.
[80]
S. Sang, Z. Yang, L. Wang, X. Liu, H. Lin, and J. Wang, SemaTyP: A knowledge graph based literature mining method for drug discovery, BMC Bioinform., vol. 19, no. 1, p. 193, 2018.
[81]
J. Yuan, Z. Jin, H. Guo, H. Jin, X. Zhang, T. Smith, and J. Luo, Constructing biomedical domain-specific knowledge graph with minimum supervision, Knowl. Inf. Syst., vol. 62, no. 1, pp. 317–336, 2020.
[82]
W. J. Vlietstra, R. Zielman, R. M. van Dongen, E. A. Schultes, F. Wiesman, R. Vos, E. M. van Mulligen, and J. A. Kors, Automated extraction of potential migraine biomarkers using a semantic graph, J. Biomed. Inform., vol. 71, pp. 178–189, 2017.
[83]
A. Rossanez, J. C. dos Reis, R. da Silva Torres, and H. de Ribaupierre, KGen: A knowledge graph generator from biomedical scientific literature, BMC Med. Inform. Decis. Mak., vol. 20, no. 4, p. 314, 2020.
[84]
D. N. Sosa, A. Derry, M. Guo, E. Wei, C. Brinton, and R. B. Altman, A literature-based knowledge graph embedding method for identifying drug repurposing opportunities in rare diseases, Pac. Symp. Biocomput., vol. 25, pp. 463–474, 2020.
[85]
Y. Song, L. Cai, K. Zhang, H. Zan, T. Liu, and X. Ren, Construction of Chinese pediatric medical knowledge graph, in 9th Joint Int. Conf. on Semantic Technology, Hangzhou, China, 2020, pp. 213–220.
[86]
G. Bakal and R. Kavuluru, Predicting treatment relations with semantic patterns over biomedical knowledge graphs, in 3rd Int. Conf. on Mining Intelligence and Knowledge Exploration, Hyderabad, India, 2015, pp. 586–596.
[87]
H. Fei, Y. Ren, Y. Zhang, D. Ji, and X. Liang, Enriching contextualized language model from knowledge graph for biomedical information extraction, Brief. Bioinform., vol. 22, no. 3, p. bbaa110, 2021.
[88]
D. Zhao, J. Wang, S. Sang, H. Lin, J. Wen, and C. Yang, Relation path feature embedding based convolutional neural network method for drug discovery, BMC Med. Inform. Decis. Mak., vol. 19, no. 2, p. 59, 2019.
[89]
C. T. Hoyt, D. Domingo-Fernández, R. Aldisi, L. Xu, K. Kolpeja, S. Spalek, E. Wollert, J. Bachman, B. M. Gyori, P. Greene, et al., Re-curation and rational enrichment of knowledge graphs in biological expression language, Database, vol. 2019, p. baz068, 2019.
[90]
T. Pham, X. Tao, J. Zhang, J. Yong, X. Zhou, and R. Gururajan, MeKG: Building a medical knowledge graph by data mining from MEDLINE, in 12th Int. Conf. on Brain Informatics, Haikou, China, 2019, pp. 159–168.
[91]
X. Yang, C. Wu, G. Nenadic, W. Wang, and K. Lu, Mining a stroke knowledge graph from literature, BMC Bioinform., vol. 22, no. 10, p. 387, 2021.
[92]
X. Chai, Diagnosis method of thyroid disease combining knowledge graph and deep learning, IEEE Access, vol. 8, pp. 149787–149795, 2020.
[93]
Y. Liu, H. Li, and Y. Chen, Using knowledge graph to handle label imperfection, in PAKDD 2014 Int. Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA on Trends and Applications in Knowledge Discovery and Data Mining, Tainan, China, 2014, pp. 345–356.
[94]
Q. Zhu, D. T. Nguyen, I. Grishagin, N. Southall, E. Sid, and A. Pariser, An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD), J. Biomed. Semant., vol. 11, no. 1, p. 13, 2020.
[95]
T. Liu, X. Pan, X. Wang, K. A. Feenstra, J. Heringa, and Z. Huang, Predicting the relationships between gut microbiota and mental disorders with knowledge graphs, Health Inf. Sci. Syst., vol. 9, no. 1, p. 3, 2021.
[96]
H. Gao, Y. Ni, X. Mo, D. Li, S. Teng, Q. Huang, S. Huang, G. Liu, S. Zhang, Y. Tang, et al., Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children, Comput. Struct. Biotechnol. J., vol. 19, pp. 3908–3921, 2021.
[97]
W. Li, L. Chai, C. Yang, and X. Wang, An evolutionary analysis of DBpedia datasets, in 15th Int. Conf. on Web Information Systems and Applications, Taiyuan, China, 2018, pp. 317–329.
[98]
N. Li, Z. Yang, L. Luo, L. Wang, Y. Zhang, H. Lin, and J. Wang, KGHC: A knowledge graph for hepatocellular carcinoma, BMC Med. Inform. Decis. Mak., vol. 20, no. 3, p. 135, 2020.
[99]
R. Zhang, D. Hristovski, D. Schutte, A. Kastrin, M. Fiszman, and H. Kilicoglu, Drug repurposing for COVID-19 via knowledge graph completion, J. Biomed. Inform., vol. 115, p. 103696, 2021.
[100]
Q. Wang, T. Wang, and C. Xu, Using a knowledge graph for hypernymy detection between Chinese symptoms, in 2018 Tenth Int. Conf. Advanced Computational Intelligence (ICACI), Xiamen, China, 2018, pp. 601–606.
[101]
S. Ganesh and A. K. Talukder, Formal methods, artificial intelligence, big-data analytics, and knowledge engineering in medical care to reduce disease burden and health disparities, in 6th Int. Conf. on Big Data Analytics, Warangal, 2018, pp. 307–321.
[102]
S. K. Mohamed, V. Nováček, and A. Nounu, Discovering protein drug targets using knowledge graph embeddings, Bioinformatics, vol. 36, no. 2, pp. 603–610, 2020.
[103]
K. Lei, K. Yuan, Q. Zhang, and Y. Shen, MedSim: A novel semantic similarity measure in bio-medical knowledge graphs, in 11th Int. Conf. on Knowledge Science, Engineering and Management, Changchun, China, 2018, pp. 479–490.
[104]
V. K. C. Yan, X. Li, X. Ye, M. Ou, R. Luo, Q. Zhang, B. Tang, B. J. Cowling, I. Hung, C. W. Siu, et al., Drug repurposing for the treatment of COVID-19: A knowledge graph approach, Adv. Ther., vol. 4, no. 7, p. 2100055, 2021.
[105]
Y. Pan, X. Lei, and Y. Zhang, Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach, Med. Res. Rev., vol. 42, no. 1, pp. 441–461, 2022.
[106]
M. Kuhn, I. Letunic, L. J. Jensen, and P. Bork, The SIDER database of drugs and side effects, Nucl. Acids Res., vol. 44, no. D1, pp. D1075–D1079, 2016.
[107]
A. L. Mitchell, T. K. Attwood, P. C. Babbitt, M. Blum, P. Bork, A. Bridge, S. D. Brown, H. Y. Chang, S. El-Gebali, M. I. Fraser, et al., InterPro in 2019: Improving coverage, classification and access to protein sequence annotations, Nucl. Acids Res., vol. 47, no. D1, pp. D351–D360, 2019.
[108]
The UniProt Consortium, UniProt: The universal protein knowledgebase, Nucl. Acids Res., vol. 45, no. D1, pp. D158–D169, 2017.
[109]
R. T. Sousa, S. Silva, and C. Pesquita, Evolving knowledge graph similarity for supervised learning in complex biomedical domains, BMC Bioinform., vol. 21, no. 1, p. 6, 2020.
[110]
Y. Yang, Z. Huang, Y. Han, X. Hua, and W. Tang, Using knowledge graph for analysis of neglected influencing factors of statin-induced myopathy, in Int. Conf. on Brain Informatics, Beijing, China, 2017, pp. 304–311.
[111]
B. Xu, Y. Xu, J. Liang, C. Xie, B. Liang, W. Cui, and Y. Xiao, CN-DBpedia: A never-ending Chinese knowledge extraction system, in 30th Int. Conf. on Industrial Engineering and Other Applications of Applied Intelligent Systems on Advances in Artificial Intelligence: From Theory to Practice, Arras, France, 2017, pp. 428–438.
[112]
F. Li, Y. Jin, W. Liu, B. P. S. Rawat, P. Cai, and H. Yu, Fine-tuning bidirectional encoder representations from transformers (BERT)-based models on large-scale electronic health record notes: An empirical study, JMIR Med. Inform., vol. 7, no. 3, p. e14830, 2019.
[113]
E. Tutubalina, Z. Miftahutdinov, S. Nikolenko, and V. Malykh, Medical concept normalization in social media posts with recurrent neural networks, J. Biomed. Inform., vol. 84, pp. 93–102, 2018.
[114]
Y. F. Luo, W. Sun, and A. Rumshisky, MCN: A comprehensive corpus for medical concept normalization, J. Biomed. Inform., vol. 92, p. 103132, 2019.
[115]
H. L. Nguyen, D. T. Vu, and J. J. Jung, Knowledge graph fusion for smart systems: A Survey, Inform. Fusion, vol. 61, pp. 56–70, 2020.
[116]
Z. Lei, Y. Sun, Y. A. Nanehkaran, S. Yang, M. S. Islam, H. Lei, and D. Zhang, A novel data-driven robust framework based on machine learning and knowledge graph for disease classification, Future Gener. Comp. Syst., vol. 102, pp. 534–548, 2020.
[117]
Y. Chen, T. Ma, X. Yang, J. Wang, B. Song, and X. Zeng, MUFFIN: Multi-scale feature fusion for drug-drug interaction prediction, Bioinformatics, vol. 37, no. 17, pp. 2651–2658, 2021.
[118]
Q. Wang, Y. Ji, Y. Hao, and J. Cao, GRL: Knowledge graph completion with GAN-based reinforcement learning, Knowl-Based. Syst., vol. 209, p. 106421, 2020.
[119]
R. Zhang, Y. Mao, and W. Zhao, Knowledge graphs completion via probabilistic reasoning, Inform. Sci., vol. 521, pp. 144–159, 2020.
[120]
X. Chen, S. Jia, and Y. Xiang, A review: Knowledge reasoning over knowledge graph, Expert Syst. Appl., vol. 141, p. 112948, 2020.
[121]
J. Chu, J. Chen, X. Chen, W. Dong, J. Shi, and Z. Huang, Knowledge-aware multi-center clinical dataset adaptation: Problem, method, and application, J. Biomed. Inform., vol. 115, p. 103710, 2021.
[122]
D. Yue, K. Zhang, L. Zhuang, X. Zhao, O. Byambasuren, and H. Zan, Annotation scheme and specification for named entities and relations on Chinese medical knowledge graph, in 20th Workshop on Chinese Lexical Semantics, Beijing, China, 2020, pp. 563–574.
[123]
N. Pattisapu, V. Anand, S. Patil, G. Palshikar, and V. Varma, Distant supervision for medical concept normalization, J. Biomed. Inform., vol. 109, p. 103522, 2020.
[124]
S. Yin, D. Chen, and J. Le, Deep neural network based on translation model for diabetes knowledge graph, in 2017 5th Int. Conf. Advanced Cloud and Big Data, Shanghai, China, 2017, pp. 318–323.
[125]
S. Biswas, P. Mitra, and K. S. Rao, Relation prediction of Co-morbid diseases using knowledge graph completion, IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 18, no. 2, pp. 708–717, 2019.
[126]
J. Santisteban and J. L. Tejada Carcamo, Unilateral Jaccard similarity coefficient, in SIGIR Workshop on Graph Search and Beyond’15, Santiago, Chile, 2015, pp. 23–27.
[127]
M. Sheng, H. Zhang, Y. Zhang, C. Li, C. Xing, J. Wang, Y. Shao, and F. Gao, CLMed: A cross-lingual knowledge graph framework for cardiovascular diseases, in 16th Int. Conf. on Web Information Systems and Applications, Qingdao, China, 2019, pp. 512–517.
[128]
M. Sheng, J. Wang, Y. Zhang, X. Li, C. Li, C. Xing, Q. Li, Y. Shao, and H. Zhang, DocKG: A knowledge graph framework for health with doctor-in-the-loopp, in 8th Int. Conf. on Health Information Science, Xi’an, China, 2019, pp. 3–14.
[129]
J. T. Reese, D. Unni, T. J. Callahan, L. Cappelletti, V. Ravanmehr, S. Carbon, K. A. Shefchek, B. M. Good, J. P. Balhoff, T. Fontana, et al., KG-COVID-19: A framework to produce customized knowledge graphs for COVID-19 response, Patterns, vol. 2, no. 1, p. 100155, 2021.
[130]
X. Xiu, Q. Qian, and S. Wu, Construction of a digestive system tumor knowledge graph based on Chinese electronic medical records: Development and usability study, JMIR Med. Inform., vol. 8, no. 10, p. e18287, 2020.
[131]
B. M. J. Some, G. Bordea G, F. Thiessard, S. Schulz, and G. Diallo, Design considerations for a knowledge graph: The WATRIMed use case, Stud. Health Technol. Inform., vol. 259, pp. 59–64, 2019.
[132]
S. Zheng, J. Rao, Y. Song, J. Zhang, X. Xiao, E. F. Fang, Y. Yang, and Z. Niu, PharmKG: A dedicated knowledge graph benchmark for bomedical data mining, Brief. Bioinform., vol. 22, no. 4, p. bbaa344, 2021.
[133]
Y. Zhou, T. Zhou, T. Zhou, H. Fu, J. Liu, and L. Shao, Contrast-attentive thoracic disease recognition with dual-weighting graph reasoning, IEEE Trans. Med. Imaging., vol. 40, no. 4, pp. 1196–1206, 2021.
[134]
X. Sun, Y. Man, Y. Zhao, J. He, and N. Liu, Incorporating description embeddings into medical knowledge graphs representation learning, in 4th Int. Conf. on Human Centered Computing, Mérida, Mexico, 2019, pp. 188–194.
[135]
Q. Li, L. Li, J. Zhong, and L. F. Huang, Real-time sepsis severity prediction on knowledge graph deep learning networks for the intensive care unit, J. Vis. Commun. Image Represent., vol. 72, p. 102901, 2020.
[136]
J. Jiang, H. Wang, J. Xie, X. Guo, Y. Guan, and Q. Yu, Medical knowledge embedding based on recursive neural network for multi-disease diagnosis, Artif. Intell. Med., vol. 103, p. 101772, 2020.
[137]
X. Wang, Y. Zhang, X. Wang, and J. Chen, A knowledge graph enhanced topic modeling approach for herb recommendation, in 24th Int. Conf. on Database Systems for Advanced Applications, Chiang Mai, Thailand, 2019, pp. 709–724.
[138]
W. J. Vlietstra, R. Vos, A. M. Sijbers, E. M. van Mulligen, and J. A. Kors, Using predicate and provenance information from a knowledge graph for drug efficacy screening, J. Biomed. Semant., vol. 9, no. 1, p. 23, 2018.
[139]
C. Moon, C. Jin, X. Dong, S. Abrar, W. Zheng, R. Y. Chirkova, and A. Tropsha, Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses, J. Biomed. Inform., vol. 119, p. 103838, 2021.
[140]
Y. Shen, K. Yuan, J. Dai, B. Tang, M. Yang, and K. Lei, KGDDS: A system for drug-drug similarity measure in therapeutic substitution based on knowledge graph curation, J. Med. Syst., vol. 43, no. 4, p. 92, 2019.
[141]
X. Liang, D. Li, M. Song, A. Madden, Y. Ding, and Y. Bu, Predicting biomedical relationships using the knowledge and graph embedding cascade model, PLoS One, vol. 14, no. 6, p. e0218264, 2019.
[142]
T. Qi, S. Qiu, X. Shen, H. Chen, S. Yang, H. Wen, Y. Zhang, Y. Wu, and Y. Huang, KeMRE: Knowledge-enhanced medical relation extraction for Chinese medicine instructions, J. Biomed. Inform., vol. 120, p. 103834, 2021.
[143]
Y. Ge, T. Tian, S. Huang, F. Wan, J. Li, S. Li, X. Wang, H. Yang, L. Hong, N. Wu, et al., An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19, Signal Transduct. Target. Ther., vol. 6, no. 1, p. 165, 2021.
[144]
J. Al-Saleem, R. Granet, S. Ramakrishnan, N. A. Ciancetta, C. Saveson, C. Gessner, and Q. Q. Zhou, Knowledge graph-based approaches to drug repurposing for COVID-19, J. Chem. Inf. Model., vol. 61, no. 8, pp. 4058–4067, 2021.
[145]
Y. Yu, M. Li, L. Liu, Y. Li, and J. Wang, Clinical big data and deep learning: Applications, challenges, and future outlooks, Big Data Mining and Analytics, vol. 2, no. 4, pp. 288–305, 2019.
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Received: 03 July 2022
Accepted: 13 July 2022
Published: 26 January 2023
Issue date: June 2023

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This work was supported in part by the National Key Research and Development Program of China (No. 2021YFF1201200), the National Natural Science Foundation of China (No. 62006251), and the Science and Technology Innovation Program of Hunan Province (No. 2021RC4008).

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