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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (9): 125-137    DOI: 10.11925/infotech.2096-3467.2021.1178
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Analyzing Medical Semantic Association with Complex Network
Zhang Junliang1,2(),Fang Xuemei1,Zhang Fan2,Liu Xiwen2,Zhu Peng3
1School of Management, Xinxiang Medical University, Xinxiang 453003, China
2Center for Health Information Resources, Xinxiang Medical University, Xinxiang 453003, China
3School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
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Abstract  

[Objective] This paper aims to study medical semantic association with the help of complex network. [Methods] First, we constructed a medical semantic association network using the medical semantic concepts as nodes and semantic associations as edges. Then, we analyzed the network characteristics and semantic community. Finally, we created vectors for the semantic concepts and conducted semantic clustering analysis with the neural network. [Results] We retrieved relevant literature on “coronavirus” from MEDLINE of PubMed and built a semantic association network with 43 nodes and 877 edges. Then, we visualized the network characteristics, semantic community and semantic clusters. [Limitations] The experimental data size needs to be expanded. [Conclusions] The proposed network effectively describes the semantic association among medical concepts and benefits medical knowledge discovery services.

Key wordsSemantic Association      Complex Network      Semantic Association Network      Semantic Community      Semantic Clustering     
Received: 15 October 2021      Published: 26 October 2022
ZTFLH:  TP393  
Fund:National Social Science Fund of China(21BTQ051);National Social Science Fund of China(17CTQ026);National Natural Science Foundation of China(72174087)
Corresponding Authors: Zhang Junliang,ORCID:0000-0002-3678-8691     E-mail: junliangzhang2000@163.com

Cite this article:

Zhang Junliang, Fang Xuemei, Zhang Fan, Liu Xiwen, Zhu Peng. Analyzing Medical Semantic Association with Complex Network. Data Analysis and Knowledge Discovery, 2022, 6(9): 125-137.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1178     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I9/125

Medical Semantic Association Process Based on Complex Network
主题词 中文 频次 主题词 中文 频次
Animals 动物 5 390 Viral Vaccines 病毒疫苗 724
Humans 人类 4 109 Reverse Transcriptase
Polymerase Chain Reaction
逆转录聚合酶链反应 719
Coronavirus Infections 冠状病毒感染 3 376 Viral Proteins 病毒蛋白质类 713
SARS Virus SARS病毒 2 868 Murine hepatitis virus 鼠肝炎病毒 706
Severe Acute Respiratory Syndrome SARS 1 805 Cercopithecus aethiops 黑长尾猴 697
Coronavirus 冠状病毒 1 533 Membrane Glycoproteins 膜糖蛋白类 683
Female 女(雌)性 1 501 Chickens 678
Mice 大鼠 1 318 Vero Cells Vero细胞 642
Male 男(雄)性 1 272 Adult 成年人 624
Antibodies, Viral 抗体,病毒 1 146 Genome, Viral 基因组, 病毒 620
Molecular Sequence Data 分子序列数据 1 100 Porcine epidemic diarrhea virus 猪流行性腹泻病毒 612
Spike Glycoprotein, Coronavirus 刺突糖蛋白, 冠状病毒 1 055 Poultry Diseases 家禽疾病 611
RNA, Viral RNA, 病毒 1 054 Swine Diseases 猪疾病 559
Cell Line 细胞系 967 Base Sequence 碱基序列 556
Middle East Respiratory Syndrome
Coronavirus
中东呼吸综合征冠状病毒 944 Antiviral Agents 抗病毒药 552
Phylogeny 种系发生 923 Middle Aged 中年人 521
Infectious bronchitis virus 传染性支气管炎病毒 914 Sequence Analysis, DNA 序列分析, DNA 458
Swine 882 Nucleocapsid Proteins 核壳蛋白质类 442
Viral Envelope Proteins 病毒包膜蛋白质类 820 China 中国 432
Virus Replication 病毒复制 807 Enzyme-Linked
Immunosorbent Assay
酶联免疫吸附测定 425
Amino Acid Sequence 氨基酸序列 767 Mice, Inbred BALB C 小鼠, 近交BALB C 415
Disease Outbreaks 疾病暴发流行 731
High Frequency Subject Words of Coronavirus
Semantic Association Network on Coronavirus
节点 DC CC BC PR 节点 DC CC BC PR
Murine Hepatitis Virus 0.88 0.91 1.17 0.02 Cell Line 0.98 1.00 1.18 0.02
Infectious Bronchitis Virus 0.88 0.91 1.11 0.02 Viral Envelope Proteins 0.98 1.00 1.20 0.02
Phylogeny 0.98 1.00 1.18 0.02 Virus Replication 0.98 1.00 1.15 0.02
SARS Virus 0.98 1.00 1.14 0.02 Genome, Viral 0.98 1.00 1.12 0.02
Poultry Diseases 0.84 0.88 1.18 0.02 Nucleocapsid Proteins 0.98 1.00 1.03 0.02
Severe Acute Respiratory Syndrome 0.95 0.98 1.15 0.02 China 0.95 0.98 1.03 0.02
Mice 0.98 1.00 1.20 0.02 Reverse Transcriptase Polymerase Chain Reaction 0.98 1.00 1.03 0.02
Adult 0.84 0.88 1.17 0.02 Enzyme-Linked Immunosorbent Assay 0.98 1.00 1.03 0.02
Swine Diseases 0.84 0.88 1.14 0.02 Molecular Sequence Data 0.98 1.00 1.00 0.02
Swine 0.93 0.95 1.18 0.02 Antibodies, Viral 0.98 1.00 1.00 0.02
Chickens 0.86 0.89 1.12 0.02 Female 0.98 1.00 1.15 0.02
Viral Vaccines 0.98 1.00 1.12 0.02 Male 0.98 1.00 1.05 0.02
Disease Outbreaks 0.98 1.00 1.11 0.02 Coronavirus 0.98 1.00 1.03 0.02
RNA, Viral 0.98 1.00 1.00 0.02 Vero Cells 0.98 1.00 1.03 0.02
Mice, Inbred BALB C 0.98 1.00 1.11 0.02 Cercopithecus Aethiops 0.98 1.00 1.03 0.02
Sequence Analysis, DNA 0.98 1.00 1.17 0.02 Amino Acid Sequence 0.98 1.00 1.00 0.02
Porcine Epidemic Diarrhea Virus 0.84 0.88 1.03 0.02 Middle East Respiratory Syndrome Coronavirus 0.88 0.91 1.03 0.02
Viral Proteins 0.98 1.00 1.06 0.02 Spike Glycoprotein, Coronavirus 0.98 1.00 1.03 0.02
Membrane Glycoproteins 0.95 0.98 1.00 0.02 Humans 0.98 1.00 1.03 0.02
Base Sequence 0.98 1.00 1.18 0.02 Coronavirus Infections 0.98 1.00 1.03 0.02
Antiviral Agents 0.98 1.00 1.20 0.02 Animals 0.98 1.00 1.00 0.02
Middle Aged 0.84 0.88 1.18 0.02
Network Features of Semantic Association Network on Coronavirus
Semantic Community of Semantic Association Network on Coronavirus
Semantic Clustering of Coronavirus
[1] Semenyuk E P. Informatics in the Context of the Differentiation and Integration of Science[J]. Scientific and Technical Information Processing, 2016, 43(1): 8-19.
doi: 10.3103/S0147688216010044
[2] Miyashita S, Sengoku S. Scientometrics for Management of Science: Collaboration and Knowledge Structures and Complexities in an Interdisciplinary Research Project[J]. Scientometrics, 2021, 126(9): 7419-7444.
doi: 10.1007/s11192-021-04080-0
[3] Raghupathi V, Raghupathi W. Exploring Science-and-Technology-Led Innovation: A Cross-Country Study[J]. Journal of Innovation and Entrepreneurship, 2019, 8: 5.
doi: 10.1186/s13731-018-0097-0
[4] Jabeen S, Gao X Y, Andreae P. Semantic Association Computation: A Comprehensive Survey[J]. Artificial Intelligence Review, 2020, 53(6): 3849-3899.
doi: 10.1007/s10462-019-09781-w
[5] 苏新宁. 领域知识加工与组织模式专题研究[J]. 情报资料工作, 2021, 42(5): 5.
[5] ( Su Xinning. Monographic Study of Domain Knowledge Processing and Organization Model[J]. Information and Documentation Services, 2021, 42(5): 5.)
[6] 周涛, 张子柯, 陈关荣, 等. 复杂网络研究的机遇与挑战[J]. 电子科技大学学报, 2014, 43(1): 1-5.
[6] ( Zhou Tao, Zhang Zike, Chen Guanrong, et al. The Opportunities and Challenges of Complex Networks Research[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(1): 1-5.)
[7] Lü L Y, Chen D B, Ren X L, et al. Vital Nodes Identification in Complex Networks[J]. Physics Reports, 2016, 650: 1-63.
doi: 10.1016/j.physrep.2016.06.007
[8] de Arruda H F, Silva F N, da F Costa L, et al. Knowledge Acquisition: A Complex Networks Approach[J]. Information Sciences, 2017, 421: 154-166.
doi: 10.1016/j.ins.2017.08.091
[9] Behrouzi S, Shafaeipour Sarmoor Z, Hajsadeghi K, et al. Predicting Scientific Research Trends Based on Link Prediction in Keyword Networks[J]. Journal of Informetrics, 2020, 14(4): 101079.
doi: 10.1016/j.joi.2020.101079
[10] Tosi M D L, dos Reis J C. SciKGraph: A Knowledge Graph Approach to Structure a Scientific Field[J]. Journal of Informetrics, 2021, 15(1): 101109.
doi: 10.1016/j.joi.2020.101109
[11] Dong L J, Yao H, Li D, et al. Improving Graph Neural Network via Complex-Network-Based Anchor Structure[J]. Knowledge-Based Systems, 2021, 233: 107528.
doi: 10.1016/j.knosys.2021.107528
[12] 肖明, 邱小花, 黄界, 等. 知识图谱工具比较研究[J]. 图书馆杂志, 2013, 32(3): 61-69.
[12] ( Xiao Ming, Qiu Xiaohua, Huang Jie, et al. Comparison of Software Tools for Mapping Knowledge Domain[J]. Library Journal, 2013, 32(3): 61-69.)
[13] Fernandez-Llimos F, Salgado T M. Standardization of Pharmacy Practice Terminology and the Medical Subject Headings(MeSH)[J]. Research in Social & Administrative Pharmacy: RSAP, 2021, 17(4): 819-820.
[14] 贾君枝. 词表生态系统: 构成要素及关联关系[J]. 中国图书馆学报, 2020, 46(4): 60-68.
[14] ( Jia Junzhi. Vocabulary Ecosystem: Components and Correlations[J]. Journal of Library Science in China, 2020, 46(4): 60-68.)
[15] 王玮康, 杨国立. 支持创新驱动发展的引领型科技情报工作研究[J]. 情报理论与实践, 2021, 44(6): 50-54.
[15] ( Wang Weikang, Yang Guoli. Leading Research on Science and Technology Intelligence in Support of Innovation-Driven Development[J]. Information Studies: Theory & Application, 2021, 44(6): 50-54.)
[16] Ding Y. Scientific Collaboration and Endorsement: Network Analysis of Coauthorship and Citation Networks[J]. Journal of Informetrics, 2011, 5(1): 187-203.
pmid: 21344057
[17] 张鑫, 文奕, 许海云. 一种融合表示学习与主题表征的作者合作预测模型[J]. 数据分析与知识发现, 2021, 5(3): 88-100.
[17] ( Zhang Xin, Wen Yi, Xu Haiyun. A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration[J]. Data Analysis and Knowledge Discovery, 2021, 5(3): 88-100.)
[18] Rüdiger M S, Antons D, Salge T O. The Explanatory Power of Citations: A New Approach to Unpacking Impact in Science[J]. Scientometrics, 2021, 126(12): 9779-9809.
doi: 10.1007/s11192-021-04103-w
[19] Fu W J, Liang X, Wei F Q, et al. Understanding the Scientific Cooperation in Incidental Gallbladder Cancer: From a Bibliometric Perspective[J]. Journal of Gastrointestinal Surgery: Official Journal of the Society for Surgery of the Alimentary Tract, 2021, 25(3): 821-824.
[20] Wang X G, Wang H Y, Huang H. Evolutionary Exploration and Comparative Analysis of the Research Topic Networks in Information Disciplines[J]. Scientometrics, 2021, 126(6): 4991-5017.
doi: 10.1007/s11192-021-03963-6
[21] Jung H, Lee B G. Research Trends in Text Mining: Semantic Network and Main Path Analysis of Selected Journals[J]. Expert Systems with Applications, 2020, 162: 113851.
doi: 10.1016/j.eswa.2020.113851
[22] 代冰, 胡正银. 基于文献的知识发现新近研究综述[J]. 数据分析与知识发现, 2021, 5(4): 1-12.
[22] ( Dai Bing, Hu Zhengyin. Review of Studies on Literature-Based Discovery[J]. Data Analysis and Knowledge Discovery, 2021, 5(4): 1-12.)
[23] Pan M, Huang J X, He T T, et al. A Simple Kernel Co-Occurrence-Based Enhancement for Pseudo-Relevance Feedback[J]. Journal of the Association for Information Science and Technology, 2020, 71(3): 264-281.
doi: 10.1002/asi.24241
[24] 李祯静, 秦春秀, 赵捧未, 等. 科技文献的资源语义空间: 一种细粒度知识组织方法[J]. 情报杂志, 2019, 38(2): 158-165.
[24] ( Li Zhenjing, Qin Chunxiu, Zhao Pengwei, et al. Resource Semantic Space of Scientific Texts: A Fine Grained Method of Knowledge Organization[J]. Journal of Intelligence, 2019, 38(2): 158-165.)
[25] Zhuang C, Li W J, Xie Z, et al. A Multi-Granularity Knowledge Association Model of Geological Text Based on Hypernetwork[J]. Earth Science Informatics, 2021, 14(1): 227-246.
doi: 10.1007/s12145-020-00534-w
[26] Zhao Q, Kang Y Y, Li J Q, et al. Exploiting the Semantic Graph for the Representation and Retrieval of Medical Documents[J]. Computers in Biology and Medicine, 2018, 101: 39-50.
doi: S0010-4825(18)30227-0 pmid: 30099238
[27] 张军亮. 多维度疾病语义相似度研究[J]. 图书情报工作, 2020, 64(12): 127-135.
doi: 10.13266/j.issn.0252-3116.2020.12.014
[27] ( Zhang Junliang. Research on Semantic Similarity of Diseases Based on Multidimensional[J]. Library and Information Service, 2020, 64(12): 127-135.)
doi: 10.13266/j.issn.0252-3116.2020.12.014
[28] Chandrasekaran D, Mago V. Evolution of Semantic Similarity—A Survey[J]. ACM Computing Surveys, 2022, 54(2): 1-37.
[29] 李楠, 孙济庆, 吉久明, 等. 基于术语语义相关的知识关联方法研究[J]. 情报学报, 2015, 34(6): 608-615.
[29] ( Li Nan, Sun Jiqing, Ji Jiuming, et al. Knowledge Association Method Based on Semantic Relatedness[J]. Journal of the China Society for Scientific and Technical Information, 2015, 34(6): 608-615.)
[30] Jiang Y C, Bai W, Zhang X P, et al. Wikipedia-Based Information Content and Semantic Similarity Computation[J]. Information Processing & Management, 2017, 53(1): 248-265.
doi: 10.1016/j.ipm.2016.09.001
[31] Hussain M J, Wasti S H, Huang G J, et al. An Approach for Measuring Semantic Similarity Between Wikipedia Concepts Using Multiple Inheritances[J]. Information Processing & Management, 2020, 57(3): 102188.
doi: 10.1016/j.ipm.2019.102188
[32] 李文清, 孙新, 张常有, 等. 一种本体概念的语义相似度计算方法[J]. 自动化学报, 2012, 38(2): 229-235.
doi: 10.3724/SP.J.1004.2012.00229
[32] ( Li Wenqing, Sun Xin, Zhang Changyou, et al. A Semantic Similarity Measure Between Ontological Concepts[J]. Acta Automatica Sinica, 2012, 38(2): 229-235.)
doi: 10.3724/SP.J.1004.2012.00229
[33] Batet M, Harispe S, Ranwez S, et al. An Information Theoretic Approach to Improve Semantic Similarity Assessments Across Multiple Ontologies[J]. Information Sciences, 2014, 283: 197-210.
doi: 10.1016/j.ins.2014.06.039
[34] Grohe M. Word2Vec, Node2Vec, Graph2Vec, X2Vec: Towards a Theory of Vector Embeddings of Structured Data[C]// Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. 2020: 1-16.
[35] Zhuhadar L, Nasraoui O, Wyatt R, et al. Visual Knowledge Representation of Conceptual Semantic Networks[J]. Social Network Analysis and Mining, 2011, 1(3): 219-229.
doi: 10.1007/s13278-010-0008-2
[36] 高伊林, 闵超. 中美对“一带一路”沿线技术扩散结构比较研究[J]. 数据分析与知识发现, 2021, 5(6): 80-92.
[36] ( Gao Yilin, Min Chao. Comparing Technology Diffusion Structure of China and the US to Countries Along the Belt and Road[J]. Data Analysis and Knowledge Discovery, 2021, 5(6): 80-92.)
[37] 曲佳彬, 欧石燕, 凌洪飞. 基于深度挖掘的学术论文关联数据构建与可视化分析[J]. 情报学报, 2019, 38(6): 595-611.
[37] ( Qu Jiabin, Ou Shiyan, Ling Hongfei. Construction and Visual Analysis of Academic Paper-Linked Data Based on In-Depth Mining[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(6): 595-611.)
[38] Su M, Peng H, Li S F. A Visualized Bibliometric Analysis of Mapping Research Trends of Machine Learning in Engineering(MLE)[J]. Expert Systems with Applications, 2021, 186: 115728.
doi: 10.1016/j.eswa.2021.115728
[39] 朱旭振. 基于链路预测的推荐系统:原理、模型与算法[M]. 北京: 北京邮电大学出版社, 2018.
[39] ( Zhu Xuzhen. Principle, Model and Algorithm of Recommendation System Based on Link Prediction[M]. Beijing: Beijing University of Posts and Telecommunications Press, 2018.)
[40] 王晓光. 科学知识网络的形成与演化(Ⅰ): 共词网络方法的提出[J]. 情报学报, 2009, 28(4): 599-605.
[40] Wang Xiaoguang. Formation and Evolution of Science Knowledge Network(Ⅰ): A New Research Method Based on Co-Word Network[J]. Journal of the China Society for Scientific and Technical Information, 2009, 28(4): 599-605.)
[41] 赵一鸣, 吕鹏辉. 学科知识网络研究(Ⅲ)共词网络的结构、特征与演化[J]. 情报学报, 2014, 33(4): 358-366.
[41] Zhao Yiming, Lv Penghui. Scientific Knowledge Networks in LIS(Ⅲ): Case Study on the Structure, Characteristics and Evolution of Cowords Networks[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(4): 358-366.)
[42] da Mata A S. Complex Networks: A Mini-Review[J]. Brazilian Journal of Physics, 2020, 50(5): 658-672.
doi: 10.1007/s13538-020-00772-9
[43] 任晓龙, 吕琳媛. 网络重要节点排序方法综述[J]. 科学通报, 2014, 59(13): 1175-1197.
[43] ( Ren Xiaolong, Lv Linyuan. Review of Ranking Nodes in Complex Networks[J]. Chinese Science Bulletin, 2014, 59(13): 1175-1197.)
[44] Musto C, Lops P, de Gemmis M, et al. Context-Aware Graph-Based Recommendations Exploiting Personalized PageRank[J]. Knowledge-Based Systems, 2021, 216: 106806.
doi: 10.1016/j.knosys.2021.106806
[45] Blondel V D, Guillaume J L, Lambiotte R, et al. Fast Unfolding of Communities in Large Networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008(10): P10008.
[46] Grover A, Leskovec J. Node2Vec: Scalable Feature Learning for Networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 855-864.
[47] 王飞跃, 李晓晨, 毛文吉. 社会计算的基本方法与应用[M]. 第2版. 杭州: 浙江大学出版社, 2013.
[47] ( Wang Feiyue, Li Xiaochen, Mao Wenji. Social Computing Methods and Applications[M]. The 2nd Edition. Hangzhou: Zhejiang University Press, 2013.)
[48] igraph—The Network Analysis Package[EB/OL]. [2021-09-30]. https://igraph.org.
[49] Node2Vec[EB/OL]. [2021-12-06]. https://github.com/aditya-grover/node2vec.
[50] sklearn[EB/OL]. [2021-09-30]. https://pypi.org/project/sklearn.
[51] PyEcharts[EB/OL]. [2021-09-30]. https://pyecharts.org.
[52] 曹志冬, 曾大军, 张清鹏, 等. 新冠肺炎疫情的复杂性特征与分析研判[J]. 中国科学基金, 2020, 34(6): 675-682.
[52] ( Cao Zhidong, Zeng Dajun, Zhang Qingpeng, et al. Complex Characteristics and Situation Judgement of the COVID-19 Epidemic[J]. Bulletin of National Natural Science Foundation of China, 2020, 34(6): 675-682.)
[53] Heidari A, Jafari N, Unal M, et al. The COVID-19 Epidemic Analysis and Diagnosis Using Deep Learning: A Systematic Literature Review and Future Directions[J]. Computers in Biology and Medicine, 2022, 141: 105141.
doi: 10.1016/j.compbiomed.2021.105141
[54] Baron J, Aznar M, Mariela M, et al. Application of Network Analysis and Cluster Analysis for Better Prevention and Control of Swine Diseases in Argentina[J]. PLoS One, 2020, 15(6): e0234489.
doi: 10.1371/journal.pone.0234489
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