Research on Construction Technology and Development Status of Medical Knowledge Graph

doi:10.3778/j.issn.1002-8331.2209-0475

Abstract

Abstract: As an important branch of artificial intelligence, knowledge graph can realize comprehensive integration of medical concepts and mining potential medical knowledge due to its powerful semantic processing ability and data organization ability, which has become an important means for the development of medical intelligence. Based on this, the latest methods and features of the four processes of medical knowledge graph building：knowledge extraction, knowledge expression, knowledge fusion and knowledge reasoning are discussed, the advantages and disadvantages of different methods are deeply studied and compared, the commonly used datasets in each stage are summarized, the research status of knowledge graph in medical knowledge question and answer, clinical auxiliary diagnosis and treatment, knowledge mining of traditional Chinese medicine and drug research are reviewed, the application difficulties in each scenario are analyzed. Finally, the limitations and challenges of the existing medical knowledge graph technology are summarized and its future development is prospected.

Key words: medical knowledge graph, deep learning, knowledge extraction, ontology, knowledge reasoning

摘要： 知识图谱作为人工智能的重要分支，因其强大的语义处理能力和数据组织能力，可以全面整合医学概念、挖掘潜在医学知识，已成为医学智能化发展的重要手段。鉴于此，论述了医学知识图谱搭建中知识抽取、知识表示、知识融合、知识推理四个过程的最新方法及特点，深入研究并对比不同方法的优缺点，归纳各阶段常用数据集，梳理知识图谱在医学知识问答、临床辅助诊疗、中医知识挖掘及药物研究等方面的研究现状及各场景下的应用难点。最后总结现有医学知识图谱技术的局限性及面临的挑战，并对其未来发展进行展望。

关键词: 医学知识图谱, 深度学习, 知识抽取, 本体, 知识推理

HUANG Hexuan, WANG Xiaoyan, GU Zhengwei, LIU Jing, ZANG Yanan, SUN Xin. Research on Construction Technology and Development Status of Medical Knowledge Graph[J]. Computer Engineering and Applications, 2023, 59(13): 33-48.

黄贺瑄, 王晓燕, 顾正位, 刘静, 臧亚男, 孙歆. 医学知识图谱构建技术及发展现状研究[J]. 计算机工程与应用, 2023, 59(13): 33-48.

References

[1] 孙雨生，常凯月，朱礼军.大规模知识图谱及其应用研究[J].情报理论与实践，2018，41（11）：138-143.
SUN Y S，CHANG K Y，ZHU L J.Research on large-scale knowledge graph and its application[J].Information Studies：Theory & Application，2018，41（11）：138-143.
[2] SINGHAL A.Introducing the knowledge graph：things，not strings[J].Official Google Blog，2012，5：16.
[3] WILLIAMS A J，HARLAND L，GROTH P，et al.Open PHACTS：semantic interoperability for drug discovery[J].Drug Discovery Today，2012，17（21/22）：1188-1198.
[4] 于彤，刘静，贾李蓉，等.大型中医药知识图谱构建研究[J].中国数字医学，2015，10（3）：80-82.
YU T，LIU J，JIA L R，et al.Research on the construction of big konwledge graph for traditional Chinese medicine[J].China Digital Medicine，2015，10（3）：80-82.
[5] 范媛媛，李忠民.中文医学知识图谱研究及应用进展[J].计算机科学与探索，2022，16（10）：2219-2233.
FAN Y Y，LI Z M.Research and application progress of Chinese medical knowledge garph[J].Journal of Frontiers of Computer Science and Technology，2022，16（10）：2219-2233.
[6] 董文波，孙仕亮，殷敏智.医学知识推理研究现状与发展[J].计算机科学与探索，2022，16（6）：1193-1213.
DONG W B，SUN S L，YIN M Z.Research and development of medical knowledge graph reasoning[J].Journal of Frontiers of Computer Science and Technology，2022，16（6）：1193-1213.
[7] JI S，PAN S，CAMBRIA E，et al.A survey on knowledge graphs：representation，acquisition，and applications[J].IEEE Transactions on Neural Networks and Learning Systems，2021，33（2）：494-514.
[8] 侯梦薇，卫荣，陆亮，等.知识图谱研究综述及其在医疗领域的应用[J].计算机研究与发展，2018，55（12）：2585-2599.
HOU M W，WEI R，LU L，et al.Research review of knowledge graph and its application in medical domain[J].Journal of Computer Research and Development，2018，55（12）：2585-2599.
[9] 李丽双，郭元凯.基于CNN-BLSTM-CRF模型的生物医学命名实体识别[J].中文信息学报，2018，32（1）：116-122.
LI L S，GUO Y K.Biomedical named entity recognition with CNN-BLSTM-CRF[J].Journal of Chinese Information Processing，2018，32（1）：116-122.
[10] DEVLIN J，CHANG M W，LEE K，et al.BERT：pre-training of deep bidirectional transformers for language understanding[J].arXiv：1810.04805，2018.
[11] LEE J，YOON W，KIM S，et al.BioBERT：a pre-trained biomedical language representation model for biomedical text mining[J].Bioinformatics，2020，36（4）：1234-1240.
[12] ZHANG T，CAI Z，WANG C，et al.SMedBERT：a knowledge-enhanced pre-trained language model with structured semantics for medical text mining[J].arXiv：2108.08983，2021.
[13] 张芳丛，秦秋莉，姜勇，等.基于RoBERTa-WWM-BiLSTM-CRF的中文电子病历命名实体识别研究[J].数据分析与知识发现，2022，6（Z1）：251-262.
ZHNAG F C，QIN Q L，JIANG Y，et al.Named entity recognition for Chinese EMR with RoBERTa-WWM-biLSTM-CRF[J].Data Analysis and Knowledge Discovery，2022，6（Z1）：251-262.
[14] 秦健，侯建新，谢怡宁，等.医疗文本的小样本命名实体识别[J].哈尔滨理工大学学报，2021，26（4）：94-101.
QIN J，HOU J X，XIE Y N，et al.Few-shot named entity recognition for medical text[J].Journal of Harbin University of Science and Technology，2021，26（4）：94-101.
[15] 李正民，云红艳，王翊臻.基于BERT的多特征融合的医疗命名实体识别[J].青岛大学学报（自然科学版），2021，34（4）：23-29.
   LI Z M，YUN H Y，WANG Y Z.Medical named entity recognition based on multi festure fusion of BERT[J].Journal of Qingdao University（Natural Science Edition），2021，34（4）：23-29.
[16] LAI P T，LU Z.BERT-GT：cross-sentence n-ary relation extraction with BERT and graph transformer[J].Bioinformatics，2020，36（24）：5678-5685.
[17] ZHAO D，WANG J，ZHANG Y，et al.Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction[J].BMC Bioinformatics，2020，21：1-17.
[18] 景慎旗，赵又霖.基于医学领域知识和远程监督的医学实体关系抽取研究[J].数据分析与知识发现，2022，6（6）：105-114.
JING S Q，ZHOU Y L.Extracting medical entity relationships with domain-specific knowledge and distant supervision[J].Data Analysis and Knowledge Discovery，2022，6（6）：105-114.
[19] WANG J，CHEN X，ZHANG Y，et al.Document-level biomedical relation extraction using graph convolutional network and multihead attention：algorithm development and validation[J].JMIR Medical Informatics，2020，8（7）：e17638.
[20] LI T，XIONG Y，WANG X，et al.Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge[J].BMC Medical Informatics and Decision Making，2021，21（7）：1-10.
[21] JIN Y，LI J，LIAN Z，et al.Supporting medical relation extraction via causality-pruned semantic dependency forest[J].arXiv：2208.13472，2022.
[22] LI J Y，XU K，LI F，et al.MRN：a locally and globally mention?based reasoning network for document?level relation extraction[C]//Findings of the Association for Computational Linguistics（ACL-IJCNLP 2021），2021：1359-1370.
[23] LI L，LIAN R，LU H，et al.Document-level biomedical relation extraction based on multi-dimensional fusion information and multi-granularity logical reasoning[C]//Proceedings of the 29th International Conference on Computational Linguistics，2022：2098-2107.
[24] LUO L，YANG Z，CAO M，et al.A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature[J].Journal of Biomedical Informatics，2020，103：103384.
[25] LI L，WANG Z，QIN X，et al.Parallel interactive attention network for joint entity and relation extraction based on Chinese electronic medical record[C]//Proceedings of the 21st Chinese National Conference on Computational Linguistics，2022：222-233.
[26] 罗文龙，王勇.基于指针标注的中文医学文本实体关系抽取研究[J].计算机科学与应用，2022，12（1）：169-177.
LUO W L，WANG Y.Research on entity relation extraction of Chinese medical text based on pointer tagging framework[J].Computer Science and Application，2022，12（1）：169-177.
[27] LAI T，JI H，ZHAI C X，et al.Joint biomedical entity and relation extraction with knowledge-enhanced collective inference[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing，Bangkok，Aug 1-6，2021：6248-6260.
[28] ZHENG H，WEN R，CHEN X，et al.PRGC：potential relation and global correspondence based joint relational triple extraction[J].arXiv：2106.09895，2021.
[29] HASSAN M M，MOKHTA H M O.AutismOnt：an ontology-driven decision support for autism diagnosis and treatment[J].Egyptian Informatics Journal，2022，23（1）：95-103.
[30] ALOBAIDI M，MALIK K，SABRA S.Linked open data-based framework for automatic biomedical ontology generation[J].BMC Bioinformatics，2018，19（1）：391.
[31] SKRETA M，ARBABI A，WNAG J，et al.Automatically disambiguating medical acronyms with ontology-aware deep learning[J].Nature Communications，2021，12（1）：5319.
[32] REYES-PEA C，TOVAR M，BRAVO M，et al.An ontology network for diabetes mellitus in mexico[J].Journal of Biomedical Semantics，2021，12（1）：1-18.
[33] ALTHUBAITI S，KAFKAS ?，ABDELHAKIM M，et al.Combining lexical and context features for automatic ontology extension[J].Journal of Biomedical Semantics，2020，11（1）：1-13.
[34] 郭梦莹，周璐，孙燕.“领域本体七步法”在中医辨证推理知识库构建中的应用[J].世界科学技术-中医药现代化，2019，21（12）：2646-2651.
GUO M Y，ZHOU L，SUN Y.Application of “domian ontology seveb-step method” in the knowledge base construction of TCM differentiation[J].Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology，2019，21（12）：2646-2651.
[35] 于彤，李敬华，于琦，等.中医养生知识图谱的构建与应用[J].中国数字医学，2017，12（12）：64-66.
   YU T，LI J H，YU Q，et al.The Construction and application of knowledge mapping of health preservation of traditional Chinese medicine[J].China Digital Medicine，2017，12（12）：64-66.
[36] 付璐，李宝金，姚克宇，等.顶层本体指导下的经络腧穴语义框架构建探索研究[J].中国针灸，2022，42（9）：1064-1072.
   FU L，LI J B，YAO K Y，et al.Exploration of the construction of semantic framework of meridians and acupoints based on top-lenel ontology[J].Chinese Acupuncture & Moxibustion，2022，42（9）：1064-1072.
[37] 王松，李正钧，杨涛，等.国医大师周仲瑛辨治肺癌的中医药本体构建研究[J].世界科学技术-中医药现代化，2022，24（2）：495-501.
WANG S，LI Z J，YANG T，et al.Study on construction of ontology for diagnosis and treatment of lung cancer by professor zhou zhongying in traditional Chinese medicine[J].Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology，2022，24（2）：495-501.
[38] TUDORACHE T，NOY N F，TU S，et al.Supporting collaborative ontology development in Protégé[C]//Proceedings of the International Semantic Web Conference，2008：17-32.
[39] XIANG Z，COURTOT M，BRINKMAN R R，et al.OntoFox：web-based support for ontology reuse[J].BMC Research Notes，2010，3（1）：1-12.
[40] BORDES A，USUNIER N，GARCIA-DURAN A，et al.Translating embeddings for modeling multi-relational data[C]//Advances in Neural Information Processing Systems，2013.
[41] 周利琴.面向智慧健康的多源异构知识融合研究[D].武汉：武汉大学，2019.
ZHOU L Q.Research on multi-source heterogeneous knowledge fusion for smart health[D].Wuhan：Wuhan University，2019.
[42] XIAO H，HUANG M，HAO Y，et al.TransG：a generative mixture model for knowledge graph embedding[J].arXiv：1509.05488，2015.
[43] LIN Y，LIU Z，LUAN H，et al.Modeling relation paths for representation learning of knowledge bases[J].arXiv：1506.00379，2015.
[44] 隋国华，李陶然，刘昊，等.基于图表示学习的领域知识图谱推理技术研究[J/OL].计算机工程：1-12[2023-02-02].http：//www.ecice06.com/CN/10.19678/j.issn.1000-3428.
0065447.
SUI G H，LI T R，LIU H，et al.Research on domain knowledge graph inference technology based on graph representation learning[J/OL].Computer Engineering：1-12[2023-02-02].http：//www.ecice06.com/CN/10.19678/j.issn.
1000-3428.0065447.
[45] LIU Z，WANG X N，YU H，et al.Predict multi-type drug-drug interactions in cold start scenario[J].BMC Bioinformatics，2022，23（1）：75.
[46] NIAN Y，HU X，ZHANG R，et al.Mining on Alzheimer’s diseases related knowledge graph to identity potential AD-related semantic triples for drug repurposing[J].BMC Bioinformatics，2022，23（6）：1-15.
[47] 刘禹琪.中医名方知识图谱构建与链路预测模型的研究及应用[D].长春：东北师范大学，2021.
LIU Y Q.Researches and applications of knowledge graph and link prediction model for famous prescriptions of traditional Chinese medicine[D].Changchun：Northeast Normal University，2021.
[48] SU X，YOU Z H，HUANG D，et al.Biomedical knowledge graph embedding with capsule network for multi-label drug-drug interaction prediction[J].IEEE Transactions on Knowledge and Data Engineering，2022（1）.
[49] 林海伦，王元卓，贾岩涛，等.面向网络大数据的知识融合方法综述[J].计算机学报，2017，40（1）：1-27.
   LIN H L，WANG Z Y，JIA Y T，et al.Network big data oriented knowledge fusion methods：a survey[J].Chinese Journal of Computers，2017，40（1）：1-27.
[50] FARIA D，PESQUITA C，BALASUBRAMANI B S，et al.OAEI 2016 results of AML[C]//Proceedings of the 11th International Workshop on Ontology Matching Co-Located with the 15th International Semantic Web Conference，2016.
[51] DJEDDI W E，KHADIR M T，YAHIA S B.XMap：results for OAEI 2015[C]//Proceedings of the 10th International Workshop on Ontology Matching colocated with the 14th International Semantic Web Conference，2015：216-221.
[52] 吴子仪，李邵梅，姜梦函，等.基于自注意力模型的本体对齐方法[J].计算机科学，2022，49（9）：215-220.
WU Z Y，LI S M，JIANG M H，et al.Ontology alignment method based on self-attention[J].Computer Science，2022，49（9）：215-220.
[53] WANG P，ZOU S，LIU J，et al.Matching biomedical ontologies with GCN-based feature propagation[J].Mathematical Biosciences and Engineering，2022，19：8479-8504.
[54] WANG P，HU Y.Matching biomedical ontologies via a hybrid graph attention network[J].Frontiers in Genetics，2022，13.
[55] XUE X，ZHANG J.Matching large-scale biomedical ontologies with central concept based partitioning algorithm and adaptive compact evolutionary algorithm[J].Applied Soft Computing，2021，106：107343.
[56] 吕青，周欣，李凤莲.动态分块调节机制下的大规模解剖学本体匹配[J].计算机应用研究，2023，40（1）：136-140.
LYU Q，ZHOU X，LI F L.Large scale anatomical ontology matching under dynamic partition adjustment[J].Application Research of Computers，2023，40（1）：136-140.
[57] MA Z，ZHAO L，LI J，et al.SiBERT：a siamese-based BERT network for Chinese medical entities alignment[J].Methods，2022，205：133-139.
[58] ZHU H，XIE R，LIU Z，et al.Iterative entity alignment via knowledge embeddings[C]//Proceedings of the International Joint Conference on Artificial Intelligence（IJCAI），2017.
[59] CHEN M，TIAN Y，YANG M，et al.Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[J].arXiv：1611.03954，2016.
[60] 张春雷.基于嵌入表示的知识图谱实体对齐研究[D].长春：吉林大学，2022.
ZHANG C L.Study on embedding-based entity alignment for knowledge graphs[D].Changchun：Jilin University，2022.
[61] 廖开际，王莹.基于MuGNN模型的互联网医疗知识融合研究[J].河南科学，2021，39（12）：2014-2022.
LIAO K J，WANG Y.Research on internet medical knowledge fusion based on MuGNN model[J].Henan Science，2021，39（12）：2014-2022.
[62] ZHANG J，ZHANG Z，ZHANG H，et al.From electronic health records to terminology base：a novel knowledge base enrichment approach[J].Journal of Biomedical Informatics，2020，113：103628.
[63] 胡宇，申德荣，聂铁铮，等.面向生物医学实体链接的联合式学习方法[J].计算机学报，2022，45（4）：748-765.
HU Y，SHEN D R，NEI T Z，et al.A joint learning method for biomedical entity linking[J].Chinese Journal of Computers，2022，45（4）：748-765.
[64] ZHAO S D，LIU T，ZHAO S C，et al.A neural multi-task learning framework to jointly model medical named entity recognition and normalization[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：817-824.
[65] LIU F，SHAREGHI E，MENG Z，et al.Self-alignment pretraining for biomedical entity representations[J].arXiv：2010.11784，2020.
[66] LIU F，VULI? I，KORHONEN A，et al.Learning domain-specialised representations for cross-lingual biomedical entity linking[J].arXiv：2105.14398，2021.
[67] CHEN L，VAROQUAUX G，SUCHANEK F M.A lightweight neural model for biomedical entity linking[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2021：12657-12665.
[68] LAI T，JI H，ZHAI C X.BERT might be overkill：a tiny but effective biomedical entity linker based on residual convolutional neural networks[J].arXiv：2109.02237，2021.
[69] BASALDELLA M，LIU F，SHAREGHI E，et al.COMETA：a corpus for medical entity linking in the social media[J].arXiv：2010.03295，2020.
[70] ZHANG Z，CHEN J，CHEN X，et al.An industry evaluation of embedding-based entity alignment[J].arXiv：2010.11522，2020.
[71] 张宇，郭文忠，林森，等.深度学习与知识推理相结合的研究综述[J].计算机工程与应用，2022，58（1）：56-69.
   ZHANG Y，GUO W Z，LIN S，et al.Review on combination of deep learning and konwledge reasoning[J].Computer Engineering and Applications，2022，58（1）：56-69.
[72] 边红.医学诊断系统中专家知识发现与推理算法研究[D].秦皇岛：燕山大学，2016.
BIAN H.Knowledge discovery and reasoning algorithm study in medical diagnose expert system[D].Qinhuangdao：Yanshan University，2016.
[73] 戴韵峰.构建基于规则和案例推理融合的失眠证素辨治系统[D].广州：广州中医药大学，2021.
DAI Y F.Construct a system of differentiation and treatment of primary insomnia based on “evidence-syndrome-prescription”[D].Guangzhou：Guangzhou University of Chinese Medicine，2021.
[74] GARCIA-DURAN A，BORDES A，USUNIER N.Composing relationships with translations[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing，2015：286-290.
[75] 赵轩伟.基于医疗领域知识图谱的问答系统研究[D].哈尔滨：哈尔滨理工大学，2021.
ZHAO X W.Research on question answering system based on medical domain knowledge graph[D].Harbin：Harbin University of Science and Technology，2021.
[76] LAN Y，HE S，LIU K，et al.Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion[J].BMC Medical Informatics and Decision Making，2021，21（9）：1-12.
[77] WANG Q，JI Y，HAO Y，et al.GRL：knowledge graph completion with GAN-based reinforcement learning[J].Knowledge-Based Systems，2020，209：106421.
[78] ZHU A，OUYANG D，LIANG S，et al.Step by step：a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning[J].Knowledge-Based Systems，2022，248：108843.
[79] 李芸.智能医疗辅助诊断和问答系统关键技术研究[D].上海：华东师范大学，2020.
LI Y.The Study of intelligent assistant medical diagnosis and medical question?answer system technologies[D].Shanghai：East China Normal University，2020.
[80] JIANG J，WANG T，WANG B，et al.Gated tree-based graph attention network（GTGAT） for medical knowledge graph reasoning[J].Artificial Intelligence in Medicine，2022，130：102329.
[81] MAO C，YAO L，LUO Y.MedGCN：medication recommendation and lab test imputation via graph convolutional networks[J].Journal of Biomedical Informatics，2022，127：104000.
[82] HUANG X，ZHANG J，XU Z，et al.A knowledge graph based question answering method for medical domain[J].PeerJ Computer Science，2021，7：e667.
[83] 姜京池.基于医学知识图谱的疾病诊断与健康预测模型研究[D].哈尔滨：哈尔滨工业大学，2019.
   JIANG J C.Research on disease diagnosis and health prediction models based on medical knowledge graph[D].Harbin：Harbin Institute of Technology，2019.
[84] LIN S，ZHOU P，LIANG X，et al.Graph-evolving meta-learning for low-resource medical dialogue generation[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2021：13362-13370.
[85] 牟梓君.小儿脑瘫中医诊疗知识图谱构建及其隐性知识显性化研究[D].北京：中国中医科学院，2021.
MOU Z J.Construction of cerebral palsy knowledgw graph and explicit transformation of tacit knowledge of diagnosis and treatment based on medical records of traditional Chinese medicine[D].Beijing：China Academy of Chinese Medical Sciences，2021.
[86] 王成文，熊励.基于知识图谱的突发公共卫生事件辅助诊疗研究[J/OL].情报科学：1-12[2022-11-22].http：//www.sinomed.ac.cn/article.do?ui=9999005930.
WANG C W，XIONG L，Knowledge graph-based aided treatment for public health emergencies[J/OL].Information Science：1-12[2022-11-22].http：//www.sinomed.ac.cn/article.do?ui=9999005930.
[87] ZHAO L，LI K，PU B，et al.An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph[J].Future Generation Computer Systems，2022，135：234-243.
[88] ZHENG W，YAN L，GOU C，et al.Pay attention to doctor-patient dialogues：multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis[J].Information Fusion，2021，75：168-185.
[89] YANG S，WU X，GE S，et al.Knowledge matters：chest radiology report generation with general and specific knowledge[J].arXiv：2112.15009，2021.
[90] HU Y，WEN G，CHAPMAN A，et al.Graph-based visual-semantic entanglement network for zero-shot image recognition[J].IEEE Transactions on Multimedia，2021，24：2473-2487.
[91] 王菁薇，肖莉，晏峻峰.基于Neo4j的《伤寒论》知识图谱构建研究[J].计算机与数字工程，2021，49（2）：264-267.
   WANG J W，XIAO L，YAN J F.Research on construction of knowledge graph of treatise onfebrile diseases based on Neo4j[J].Computer & Digital Engineering，2021，49（2）：264-267.
[92] 张泠杉，王凤兰，邢琛林，等.基于知识元标引的《王旭高医案》逻辑数据及知识图谱探析[J].南京中医药大学学报，2021，37（4）：592-596.
ZHANG L S，WANG F L，XING C L，et al.An exploration of the logical data and knowledge graph of Wang Xugao’s case records based on knowledge element indexing[J].Journal of Nanjing University of Traditional Chinese Medicine，2021，37（4）：592-596.
[93] 石燕，何黎，任秋静，等.中医体质知识图谱分析——基于VOSviewer和CiteSpace的计量分析[J].世界科学技术-中医药现代化，2021，23（9）：3415-3423.
   SHI Y，HE L，REN Q J，et al.Analysis of knowledge graph of traditional Chinese medicine composition：a quantitative analysis based on VOSviewer and CiteSpace[J].Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology，2021，23（9）：3415-3423.
[94] 尹丹，周璐，周雨玫，等.中医经方知识图谱“图搜索模式”设计研究[J].中国中医药信息杂志，2019，26（8）：94-98.
   YIN D，ZHOU L，ZHOU Y M，et al.Study on design of graph search pattern of knowledge graph of tcm classic prescriptions[J].Chinese Journal of Information on Traditional Chinese Medicine，2019，26（8）：94-98.
[95] 金连顺，张曈，何伟炎，等.基于知识图谱构建和定性访谈法探析张忠德教授辨治间质性肺病临床特征与方药规律[J].世界科学技术-中医药现代化，2021，23（8）：2838-2848.
   JIN L S，ZHANG T，HE W Y，et al.An analysis of clinical characteristics and prescription patterns of Professor Zhang Zhongde’s treatment of interstitial lung disease based on knowledge mapping and qualitative interview[J].Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology，2021，23（8）：2838-2848.
[96] 孙明俊，张丹，郑明智，等.基于人工智能的类风湿性关节炎中医辅助诊疗系统[J].模式识别与人工智能，2021，34（4）：343-352.
   SUN M J，ZHANG D，ZHENG M Z，et al.Traditional Chinese medicine aided diagnosis and treatment system for rheumatoid arthritis based on artificial intelligence[J].Pattern Recognition and Artificial Intelligence，2021，34（4）：343-352.
[97] 刘琦.中医脉诊科技文献的知识抽取研究[D].长沙：湖南中医药大学，2021.
   LIU Q.Research on knowledge extraction from scientific literature of pulse diagnosis in traditional Chinese medicine[D].Changsha：Hunan University of Chinese Medicine，2021.
[98] SANG S，YANG Z，LIU X，et al.GrEDeL：a knowledge graph embedding based method for drug discovery from biomedical literatures[J].IEEE Access，2018，7：8404-8415.
[99] GAO Z，DING P，XU R.KG-predict：a knowledge graph computational framework for drug repurposing[J].Journal of Biomedical Informatics，2022，132：104133.
[100] JOSHI P，MASILAMANI V，MUKHERJEE A.A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network[J].Journal of Biomedical Informatics，2022，132：104122.
[101] SAKOR A，JOZASHOORI S，NIAZMAND E，et al.Knowledge4COVID-19：a semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments’ toxicities[J].Journal of Web Semantics，2023，75：100760.
[102] 曾子玲，佟琳，刘思鸿，等.基于CiteSpace知识图谱的麦冬研究热点与趋势分析[J].中国中药杂志，2021，46（24）：6549-6557.
ZENG Z L，TONG L，LIU S H，et al.Hotspots and trends of ophiopogonis radix based on CiteSpace knowledge map[J].China Journal of Chinese Materia Medica，2021，46（24）：6549-6557.
[103] 廖鑫，王适，蔡媛，等.基于知识图谱分析中药灭菌未来发展趋势[J].辐射研究与辐射工艺学报，2022，40（6）：31-41.
LIAO X，WANG S，CAI Y，et al.Knowledge graph analysis of current situation and trend in Chinese madicine sterilization[J].Journal of Radiation Research and Radiation Processing，2022，40（6）：31-41.
[104] 王俊文，叶壮志.人工智能技术在中医诊断领域应用述评[J].世界科学技术-中医药现代化，2022，24（2）：810-814.
WANG J W，YE Z Z.Review on the application of artificial intelligence technology in the field of traditional Chinese medicine diagnosis[J].Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology，2022，24（2）：810-814.