Research and Development of Medical Knowledge Graph Reasoning

doi:10.3778/j.issn.1673-9418.2111031

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (6): 1193-1213.DOI: 10.3778/j.issn.1673-9418.2111031

• Surveys and Frontiers • Previous Articles Next Articles

Research and Development of Medical Knowledge Graph Reasoning

DONG Wenbo¹, SUN Shiliang¹^,⁺(), YIN Minzhi²

1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
2. Department of Pathology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

Received:2021-11-04 Revised:2022-01-20 Online:2022-06-01 Published:2022-06-20
About author:DONG Wenbo, born in 1992, Ph.D. candidate. His research interests include pattern recognition and machine learning, knowledge graph reasoning, etc.
SUN Shiliang, born in 1979, Ph.D., professor, Ph.D. supervisor, member of CCF. His research interests include multi-view learning, pattern recognition and machine learning.
YIN Minzhi, born in 1970, chief physician. Her research interests include the pathological diagnosis of children’s tumors and the application of AI in pathological diagnosis.
Supported by:
National Natural Science Foundation of China(62076096);Project of Shanghai Knowledge Service Platform(ZF1213)

医学知识推理研究现状与发展

董文波¹, 孙仕亮¹^,⁺(), 殷敏智²

1. 华东师范大学计算机科学与技术学院,上海 200062
2. 上海交通大学医学院附属上海儿童医学中心病理科,上海 200127

通讯作者: + E-mail: slsun@cs.ecnu.edu.cn
作者简介:董文波（1992—）,男,河南新乡人,博士研究生,主要研究方向为模式识别与机器学习、知识图谱推理等。
孙仕亮（1979—）,男,博士,教授,博士生导师,CCF会员,主要研究方向为多视图学习、模式识别、机器学习。
殷敏智（1970—）,女,浙江宁波人,主任医师,主要研究方向为儿童肿瘤的病理诊断、AI在病理诊断中的应用。
基金资助:
国家自然科学基金(62076096);上海知识服务平台项目(ZF1213)

Abstract

Abstract:

Knowledge graphs can effectively organize and represent knowledge, which have been applied to many advanced applications, for example, intelligent medicine. However, the medical knowledge graphs constructed manually or automatically are usually incomplete, which seriously limits their performance. Medical knowledge reasoning can complete medical knowledge graph and assist doctors in medical diagnosis. This paper first gives the basic concept and definition of medical knowledge reasoning, and then summarizes the key technologies of constructing medical knowledge graphs and the auxiliary diagnosis methods based on medical knowledge reasoning. Subsequently, this paper reviews the research development of medical knowledge reasoning, and classifies its reasoning methods into rule-based medical reasoning, representation learning-based medical reasoning and deep learning-based medical reasoning. For each category, representative algorithms and newly proposed algorithms are presented. The main feature of this survey is that it provides a comprehensive introduction for the recent development of knowledge graph reasoning on the basis of coherence with early methods. Finally, this paper prospects the development of medical knowledge reasoning based on the major challenges and key problems faced by medical knowledge reasoning, hoping to promote further research in this rapidly developing field.

Key words: knowledge graph, intelligent medicine, knowledge reasoning, medical assistant diagnosis, medical knowledge completion

摘要：

知识图谱可以有效地组织和表示知识,被应用于很多高级应用中,比如智能医疗。然而,无论是人工还是自动化构建的医学知识图谱通常是不完整的,这严重限制了它们的使用性能。医学知识推理可以补全医学知识图谱,并可辅助医生进行医学诊断。首先给出了医学知识推理的基本概念和定义,然后对构建医学知识图谱的关键技术和基于医学知识推理的辅助诊断进行了总结与归纳,并重点回顾了医学知识推理研究现状,将其推理方法划分为基于逻辑规则的医学推理、基于表示学习的医学推理以及基于深度学习的医学推理。对于每一类别,分别介绍了代表性算法和最新研究进展。特点是在现有方法的基础上对基于医学知识图谱的推理技术进行了综合的介绍。最后总结了医学知识推理目前面对的一些挑战和重要问题,并展望了其发展前景和研究趋势,希望能促进这一快速发展领域的进一步研究。

关键词: 知识图谱, 智能医疗, 知识推理, 医学辅助诊断, 医学知识补全

CLC Number:

TP181
TP399

DONG Wenbo, SUN Shiliang, YIN Minzhi. Research and Development of Medical Knowledge Graph Reasoning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1193-1213.

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.

Figures/Tables 8

References 138

[1]	NICKEL M, TRESP V, KRIEGEL H P. A three-way model for collective learning on multi-relational data[C]// Proceedings of the 28th International Conference on Machine Learning, Bellevue, Jun 28-Jul 2, 2011. Madison: Omni Press, 2011: 809-816.
[2]	AUER S, BIZER C, KOBILAROV G, et al. DBpedia: a nucleus for a web of open data[C]// Proceedings of the 6th International Semantic Web Conference on Semantic Web, Busan, Nov 11-15, 2007. Berlin, Heidelberg: Springer, 2007: 722-735.
[3]	BOLLACKER K D, EVANS C, PARITOSH P K, et al. Free-base: a collaboratively created graph database for structuring human knowledge[C]// Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Jun 10-12, 2008. New York: ACM, 2008: 1247-1250.
[4]	SUCHANEK F M, KASNECI G, WEIKUM G. YAGO: a core of semantic knowledge[C]// Proceedings of the 16th Inter-national Conference on World Wide Web, Banff, May 8-12, 2007. New York: ACM, 2007: 697-706.
[5]	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): 2587-2599.
	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): 2587-2599.
[6]	盛明, 陈玉思, 张勇, 等. 一种面向医疗健康领域知识图谱的可扩展系统架构的研究[J]. 小型微型计算机系统, 2019, 40(10): 2150-2154.
	SHENG M, CHEN Y S, ZHANG Y, et al. Research of an extensible framework for health knowledge graph[J]. Journal of Chinese Computer Systems, 2019, 40(10): 2150-2154.
[7]	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.
[8]	SULAKHE D, BALASUBRAMANIAN S, XIE B Q, et al. Lynx: a database and knowledge extraction engine for integ-rative medicine[J]. Nucleic Acids Research, 2014, 42(1): 1007-1012.
[9]	OGISHIMA S, TAKAI T, SHIMOKAWA K. Integrated data- base and knowledge base for genomic prospective cohort study in Tohoku medical megabank toward personalized prevention and medicine[C]// Proceedings of the 15th World Congress on Health and Biomedical Informatics, São Paulo, Aug 19-23, 2015. Amsterdam: IOS Press, 2015: 1057-1065.
[10]	贾李蓉, 刘静, 于彤. 中医药知识图谱构建[J]. 医学信息学杂志, 2015, 36(8): 51-55.
	JIA L R, LIU J, YU T. Construction of traditional Chinese medicine knowledge graph[J]. Journal of Medical Intelligence, 2015, 36(8): 51-55.
[11]	王俏, 王伟. 基于知识图谱的国际基因组流行病学可视化分析[J]. 中华医学图书情报杂志, 2013, 22(4): 2-9.
	WANG Q, WANG W. Papers on genome epidemiology in the world: a knowledge map-based visual analysis[J]. Chinese Journal of Medical Library and Information Science, 2013, 22(4): 2-9.
[12]	康莉. 基于知识图谱的心血管病问答系统的研究与实现[D]. 广州: 华南理工大学, 2020.
	KANG L. Research and implementation of cardiovascular disease question answering system based on knowledge graph[D]. Guangzhou: South China University of Technology, 2020.
[13]	ZHANG B. Theory and applications of problem solving[M]. London: Elsevier Science Inc., 1992.
[14]	NIKOLAS K. So we need something else for reason to mean[J]. International Journal of Philosophical Studies, 2000, 8(3): 271-295. DOI URL
[15]	TARI L. Knowledge inference[M]//Encyclopedia of Systems Biology. Berlin, Heidelberg: Springer, 2013: 1074-1078.
[16]	吴运兵, 杨帆, 赖国华. 知识图谱学习和推理研究进展[J]. 小型微型计算机系统, 2016, 37(9): 2007-2013.
	WU Y B, YANG F, LAI G H. Research progress of knowledge graph learning and reasoning[J]. Journal of Chinese Computer Systems, 2016, 37(9): 2007-2013.
[17]	OSHEROFF J, TEICH J, LEVICK D. Improving outcomes with clinical decision support: an implementer’s guide[M]. Chicago: Healthcare Information and Management Systems Society, 2012.
[18]	袁凯琦, 邓扬, 陈道源. 医学知识图谱构建技术与研究进展[J]. 计算机应用研究, 2018, 35(7): 1929-1936.
	YUAN K Q, DENG Y, CHEN D Y. Construction techniques and research development of medical knowledge graph[J]. Application Research of Computers, 2018, 35(7): 1929-1936.
[19]	刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600.
	LIU Q, LI Y, DUAN H, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582-600.
[20]	侯丽, 钱庆, 黄利辉, 等. 基于本体的临床医学知识库系统构建探讨[J]. 医学信息杂志, 2011, 32(4): 42-47.
	HOU L, QIAN Q, HUANG L H, et al. Discussion on clinical medicine knowledge base system construction based on ontology[J]. Journal of Medical Information, 2011, 32(4): 42-47.
[21]	刘权. 基于神经网络的自然语言语义表达及推理方法研究[D]. 合肥: 中国科学技术大学, 2017.
	LIU Q. Research on natural language semantic representation and reasoning based on neural networks[D]. Hefei: University of Science and Technology of China, 2017.
[22]	WU S T, LIU H F, LI D C, et al. Unified medical language system term occurrences in clinical notes: a large-scale corpus analysis[J]. Journal of the American Medical Informatics Association, 2012, 19: 149-156. DOI URL
[23]	FRIEDMAN C, ALDERSON P O, AUSTIN J H M, et al. A general natural-language text processor for clinical radiology[J]. Journal of the American Medical Informatics Association, 1994, 1(2): 161-174. DOI URL
[24]	HANISCH D, FUNDEL K, MEVISSEN H T, et al. ProMiner: rule-based protein and gene entity recognition[J]. BMC Bioin- formatics, 2005, 6(1): 1-9.
[25]	SAVOVA G K, MASANZ J J, OGREN P V, et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications[J]. Journal of the American Medical Informatics Association, 2010, 17(5): 507-513. DOI URL
[26]	YANG Z, LIN H, LI Y. Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature[J]. Computational Biology and Chemistry, 2008, 32(4): 287-291. DOI URL
[27]	KAZAMA J, MAKINO T, OHTA Y, et al. Tuning support vector machines for biomedical named entity recognition[C]// Proceedings of the ACL 2002 Workshop on Natural Language Processing in the Biomedical Domain, Philadelphia, Jul 11, 2002. Stroudsburg: ACL, 2002: 1-8.
[28]	ZHOU G D, ZHANG J, SU J, et al. Recognizing names in biomedical texts: a machine learning approach[J]. Bioinfor-matics, 2004, 20(7): 1178-1190.
[29]	TANG B, CAO H, WU Y, et al. Recognizing clinical entities in hospital discharge summaries using structural support vector machines with word representation features[J]. BMC Medical Informatics & Decision Making, 2013, 13(1): 1-10.
[30]	COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12: 2493-2537.
[31]	WEI Q K, CHEN T, XU R F, et al. Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks[J]. Database—The Journal of Biological Databases and Curation, 2016: 1-8. DOI: 10.1093/database/baw140. DOI
[32]	JAGANNATHA A, YU H. Structured prediction models for RNN based sequence labeling in clinical text[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Nov 1-4, 2016. Stroudsburg:ACL, 2016: 856-865.
[33]	UZUNERÖ, MAILOA J, RYAN R, et al. Semantic relations for problem-oriented medical records[J]. Artificial Intelligence in Medicine, 2010, 50(2): 63-73. DOI URL
[34]	ABACHA A B, ZWEIGENBAUM P. A hybrid approach for the extraction of semantic relations from MEDLINE abstracts[C]// Proceedings of the 12th International Conference on Com-putational Linguistics and Intelligent Text Processing, Tokyo, Feb 20-26, 2011. Berlin, Heidelberg: Springer, 2011: 139-150.
[35]	RYAN R J. Groundtruth budgeting: a novel approach to semi-supervised relation extraction in medical language[D]. Mass-achusetts Institute of Technology, 2011.
[36]	曹明宇, 杨志豪, 罗凌, 等. 基于神经网络的药物实体与关系联合抽取[J]. 计算机研究与发展, 2019, 56(7): 1432-1440.
	CAO M Y, YANG Z H, LUO L, et al. Joint drug entities and relations extraction based on neural networks[J]. Journal of Computer Research and Development, 2019, 56(7): 1432-1440.
[37]	SEOL J W, YI W J, CHOI J, et al. Causality patterns and machine learning for the extraction of problem-action relations in discharge summaries[J]. International Journal of Medical Informatics, 2017, 98: 1-12. DOI URL
[38]	阮彤, 王梦婕, 王昊奋. 垂直知识图谱的构建与应用研究[J]. 知识管理论坛, 2016(3): 226-234.
	RUAN T, WANG M J, WANG H F. Research on the constr-uction and application of vertical knowledge graphs[J]. Knowledge Management Forum, 2016(3): 226-234.
[39]	刘志远, 孙茂松, 林衍凯. 知识表示学习研究进展[J]. 计算机研究与发展, 2016, 53(2): 247-261.
	LIU Z Y, SUN M S, LIN Y K. Knowledge representation learning: a review[J]. Journal of Computer Research and Development, 2016, 53(2): 247-261.
[40]	于晓青, 曹慧, 魏德健. 数据融合技术及其在医学领域的应用[J]. 中国医疗设备, 2017, 32(3): 99-102.
	YU X Q, CAO H, WEI D J. Data fusion technology and its application in medical field[J]. China Medical Devices, 2017, 32(3): 99-102.
[41]	翟霄, 潘海为, 谢晓芹, 等. 支持多模态医学数据融合的并行加载算法[J]. 数据采集与处理, 2018, 33(4): 758-768.
	ZHAI X, PAN H W, XIE X Q, et al. Parallel loading algorithm for multi-mode medical data fusion[J]. Journal of Data Acquisition and Processing, 2018, 33(4): 758-768.
[42]	吴嘉敏. 肺癌医学知识图谱的构建与分析[D]. 银川: 宁夏大学, 2019.
	WU J M. Construction and analysis of medical knowledge graph of lung cancer[D]. Yinchuan: Ningxia University, 2019.
[43]	庞震, 刘剑. 基于多数据源融合的医疗知识图谱框架构建研究[J]. 科学与信息化, 2019(35): 115.
	PANG Z, LIU J. Research on the construction of medical knowledge graph framework based on multi-data source fusion[J]. Technology and Information, 2019(35): 115.
[44]	张志剑. 基于深度学习的高血压知识图谱构建研究[D]. 太原: 中北大学, 2020.
	ZHANG Z J. Research on construction of hypertension knowledge graph based on deep learning[D]. Taiyuan: North University of China, 2020.
[45]	DIENG K R, MINIER D, RŮŽIČKA M, et al. Building and using a medical ontology for knowledge management and cooperative work in a health care network[J]. Computers in Biology and Medicine, 2006, 36(7/8): 871-892. DOI URL
[46]	BAORTO D, LI L, CIMINO J J. Practical experience with the maintenance and auditing of a large medical ontology[J]. Journal of Biomedical Informatics, 2009, 42(3): 494-503. DOI URL
[47]	王硕, 杜志娟, 孟小峰. 大规模知识图谱补全技术的研究进展[J]. 中国科学: 信息科学, 2020, 50(4): 551-575.
	WANG S, DU Z J, MENG X F. Research progress of large-scale knowledge graph completion technology[J]. Science in China: Information Sciences, 2020, 50(4): 551-575. DOI URL
[48]	CLARKE E L, LOGUERCIO S, GOOD B M, et al. A task-based approach for gene ontology evaluation[J]. Journal of Biomedical Semantics, 2013, 4(S1): S4. DOI URL
[49]	BRIGHT T J, FURUYA E Y, KUPERMAN G J, et al. Development and evaluation of an ontology for guiding appropriate antibiotic prescribing[J]. Journal of Biomedical Informatics, 2012, 45(1): 120-128. DOI URL
[50]	GORDON C L, POUCH S, COWELL L G, et al. Design and evaluation of a bacterial clinical infectious diseases ontology[C]// Proceedings of the American Medical Informatics Association Annual Symposium, Washington, Nov 16-20, 2013: 502-511.
[51]	昝红英, 窦华溢, 贾玉祥, 等. 基于多来源文本的中文医学知识图谱的构建[J]. 郑州大学学报(理学版), 2020, 52(2): 45-51.
	ZAN H Y, DOU H Y, JIA Y X, et al. Construction of Chinese medical knowledge graph based on multi-source corpus[J]. Journal of Zhengzhou University (Natural Science Edition), 2020, 52(2): 45-51.
[52]	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 on febrile diseases based on Neo4j[J]. Computer and Digital Engineering, 2021, 49(2): 264-267.
[53]	刘忠宝, 张志剑, 赵文娟. 大数据环境下高血压知识库构建与系统集成方法研究[J]. 医学信息学杂志, 2020, 41(10): 37-42.
	LIU Z B, ZHANG Z J, ZHAO W J. Study on the building of hypertension knowledge base and system integration method under the big data environment[J]. Journal of Medical Informatics, 2020, 41(10): 37-42.
[54]	LI L, ZHANG P, ZHENG T, et al. Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures[J]. PLoS One, 2014, 9(3): 91-98.
[55]	YANG Z, ZHAO Z, LI Y, et al. PPIExtractor: a protein interaction extraction and visualization system for biomedical literature[J]. IEEE Transactions on Nanobioscience, 2013, 12(3): 173-181. DOI URL
[56]	ZHANG Y, LIN H, YANG Z, et al. A hybrid model based on neural networks for biomedical relation extraction[J]. Journal of Biomedical Informatics, 2018, 81: 83-92. DOI URL
[57]	WANG P, WU Q, SHEN C, et al. Explicit knowledge-based reasoning for visual question answering[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Aug 19-25, 2017. Palo Alto: AAAI, 2017: 1290-1296.
[58]	PIÑERO J, BRAVO À, QUERALT-ROSINACH N. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants[J]. Nucleic Acids Research, 2017, 45: 833-839.
[59]	如虹, 崔文成. 医疗诊断专家系统研究进展[J]. 小型微型计算机系统, 2003, 24(3): 509-512.
	RU H, CUI W C. Research advances on medical diagnosis expert system[J]. Journal of Chinese Computer Systems, 2003, 24(3): 509-512.
[60]	涂志松. 使用JESS开发基于Web的中医诊断专家系统研究[D]. 郑州: 郑州大学, 2010.
	TU Z S. A research on Web-based Chinese medical diagnosis expert system developed with JESS[D]. Zhengzhou: Zhengzhou University, 2010.
[61]	谈耀永. 基于规则计算机专家系统的脏腑相关模型研究[D]. 广州: 广州中医药大学, 2014.
	TAN Y Y. A computer rule-based expert system model investigation for correlations between the Zang-fu organs[D]. Guangzhou: Guangzhou University of Chinese Medicine, 2014.
[62]	董冬. 基于案例推理的医学图像智能诊断系统研究[D]. 哈尔滨: 哈尔滨理工大学, 2009.
	DONG D. Research of intelligent identification and diagnosis system of medical image based on CBR[D]. Harbin: Harbin University of Science and Technology, 2009.
[63]	PETERS L, BAHR N, BODENREIDER O. Evaluating drug-drug interaction information in NDF-RT and DrugBank[J]. Journal of Biomedical Semantics, 2015, 6(1): 19-27. DOI URL
[64]	KUHN M, LETUNIC I, JENSEN L J, et al. The SIDER database of drugs and side effects[J]. Nucleic Acids Research, 2016, 44: 1075-1079.
[65]	WEI W Q, MOSLEY J D, BASTARACHE L. Validation and enhancement of a computable medication indication resource (MEDI) using a large practice-based dataset[C]// Proceedings of the American Medical Informatics Association Annual Symposium, Washington, Nov 16-20, 2013: 1448-1456.
[66]	KHARE R, LI J, LU Z Y. LabeledIn: cataloging labeled indications for human drugs[J]. Journal of Biomedical In-formatics, 2014, 52: 448-456.
[67]	KASTRIN A, RINDFLESCH T, HRISTOVSKI D. Link prediction on the semantic MEDLINE network[C]// Proce-edings of the 2014 International Conference on Discovery Science. Cham: Springer, 2014: 135-143.
[68]	SOUSA A M, PEREIRA M O, LOURENÇO A. MorphoCol: an ontology-based knowledge base for the characterisation of clinically significant bacterial colony morphologies[J]. Journal of Biomedical Informatics, 2015, 55: 55-63. DOI URL
[69]	ABRAHAM I, BUCKWALTER K. Geropsychiatric nursing: a clinical knowledge base in community and institutional settings[J]. Journal of Psychosocial Nursing and Mental Health Services, 1994, 32(4): 20-26.
[70]	孙肇阳, 黄小圆, 许溪彬, 等. 语义知识图谱与科学知识图谱在中医药领域的应用[J]. 医学信息学杂志, 2021, 42(7): 38-42.
	SUN Z Y, HUANG X Y, XU X B, et al. Application of semantic knowledge graph and mapping knowledge domain in the field of traditional chinese medicine[J]. Journal of Medical Informatics, 2021, 42(7): 38-42.
[71]	张德政, 彭嘉宁, 范红霞. 中医专家系统技术综述及新系统实现研究[J]. 计算机应用研究, 2007, 24(12): 6-9.
	ZHANG D Z, PENG J N, FAN H X. Present research situation and prospect of Chinese medicine expert system[J]. Application Research of Computers, 2007, 24(12): 6-9.
[72]	杨连初, 朱文锋. 中医诊疗标准软件(TCMDSS)的研究及其实现[J]. 中医药导报, 1999(5): 44-45.
	YANG L C, ZHU W F. Research and implementation of traditional Chinese medicine diagnosis and treatment standard software[J]. Guiding Journal of Traditional Chinese Medicine and Pharmacy, 1999(5): 44-45.
[73]	阮彤, 孙程琳, 王昊奋. 中医药知识图谱构建与应用[J]. 医学信息学杂志, 2016, 37(4): 8-13.
	RUAN T, SUN C L, WANG H F. Construction of traditional Chinese medicine knowledge graph and its application[J]. Journal of Medical Intelligence, 2016, 37(4): 8-13.
[74]	郑少宇. 基于临床医学知识图谱的常见病诊断辅助系统[D]. 成都: 西南交通大学, 2020.
	ZHENG S Y. Common disease diagnosis support system based on clinical medical knowledge graph[D]. Chengdu: Southwest Jiaotong University, 2020.
[75]	LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]// Proceedings of the 29th AAAI Conference on Artificial In-telligence, Austin, Jan 25-30, 2015. Palo Alto: AAAI, 2015: 2181-2187.
[76]	GARDNER M, MITCHELL T M. Efficient and expressive knowledge base completion using subgraph feature extraction[C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Sep 17-21, 2015. Stroudsburg: ACL, 2015: 1488-1498.
[77]	GARCÍA-DURÁN A, BORDES A, USUNIER N, et al. Combining two and three-way embedding models for link prediction in knowledge bases[J]. Computer Science, 2016, 55: 715-742.
[78]	SCHOENMACKERS S, DAVIS J, ETZIONI O, et al. Learning first-order horn clauses from web text[C]// Proceedings of the 2010 Conference on Empirical Methods in Natural Lang-uage Processing, Cambridge, Oct 9-11, 2010. Stroudsburg: ACL, 2010: 1088-1098.
[79]	BIENVENU M, BOURGAUX C, GOASDOUÉ F. Computing and explaining query answers over inconsistent DL-Lite know-ledge bases[J]. Journal of Artificial Intelligence Research, 2019, 64: 563-644. DOI URL
[80]	WANG W Y, MAZAITIS K, LAO N, et al. Efficient inference and learning in a large knowledge base-reasoning with extracted information using a locally groundable first-order probabilistic logic[J]. Machine Language, 2015, 100(1): 101-126.
[81]	YANG F, YANG Z L, COHEN W W. Differentiable learning of logical rules for knowledge base reasoning[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates, 2017: 2319-2328.
[82]	BOUSQUET C, HENEGAR C, LOUET L, et al. Impleme-ntation of automated signal generation in pharmacovigilance using a knowledge-based approach[J]. International Journal of Medical Informatics, 2005, 74(8): 563-571. DOI URL
[83]	CHEN R, HUANG Y, BAU C, et al. A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection[J]. Expert Systems with Applications, 2012, 39(4): 3995-4006. DOI URL
[84]	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.
[85]	李甦, 袁勇. 医学专家系统中知识表示、获取和推理的两种方法[J]. 计算机工程与应用, 2002, 38(2): 210-213.
	LI S, YUAN Y. Two methods of knowledge representation, knowledge acquisition and knowledge reasoning for a medical expert system[J]. Computer Engineering and Applications, 2002, 38(2): 210-213.
[86]	罗率力. 基于医学诊断的模糊专家系统技术研究[D]. 长沙: 湖南大学, 2012.
	LUO S L. The medical diagnosis based on fuzzy expert system technology research[D]. Changsha: Hunan University, 2012.
[87]	KUMAR K, SINGH Y, SANYAL S. Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU[J]. Expert Systems with Applications, 2009, 36(1): 65-71. DOI URL
[88]	MARKUS K, MAXIMILIAN M, ANA O, et al. Attributed description logics: reasoning on knowledge graphs[C]// Proceedings of the 27th International Join Conference on Artificial Intelligence, Stockholm, Jul 13-19, 2018. Menlo Park: AAAI, 2018: 5309-5313.
[89]	WEI Y Z, LUO J, XIE H Y. KGRL:an OWL2 RL reasoning system for large scale knowledge graph[C]// Proceedings of the 2016 12th International Conference on Semantics, Knowledge and Grid, Beijing, Aug 15-17, 2016. Washington: IEEE Computer Society, 2016: 83-89.
[90]	ROMERO M M, VÁZQUEZ-NAYA J M, PEREIRA J, et al. The iOSC 3 system: using ontologies and SWRL rules for intelligent supervision and care of patients with acute cardiac disorders[J]. Computational and Mathematical Methods in Medicine, 2013: 650671. DOI: 10.1155/2013/650671. DOI
[91]	SHI L, LI S, YANG X. Semantic health knowledge graph: semantic integration of heterogeneous medical knowledge and services[J]. BioMed Research International, 2017: 2858423. DOI: 10.1155/2017/2858423. DOI
[92]	ANGEL G, ALEJANDRO R, MYRIAM M, et al. ODDIN: ontology-driven differential diagnosis based on logical infer- ence and probabilistic refinements[J]. Expert Systems with Application, 2010, 37(3): 2621-2628. DOI URL
[93]	LAO N, WILLIAM W. Relational retrieval using a combin-ation of path-constrained random walks[J]. Machine Learning, 2020, 81(1): 53-67. DOI URL
[94]	侯秀萍, 袁秀丽, 姜卓, 等. 不确定性推理技术在医学诊断中的应用研究[J]. 计算机工程与应用, 2005, 41(14): 205-207.
	HOU X P, YUAN X L, JIANG Z, et al. Research on uncert-ainty reasoning technology in medical diagnosis[J]. Computer Engineering and Applications, 2005, 41(14): 205-207.
[95]	朱吕行. 面向生物医学文本及图谱的知识挖掘与知识发现[D]. 合肥: 中国科学技术大学, 2019.
	ZHU L X. Knowledge mining and knowledge discovering for biomedical text and graph[D]. Hefei: University of Science and Technology of China, 2019.
[96]	王雁, 姚梅林. 专家医生的知识结构及诊断推理方式[J]. 心理科学进展, 2009, 17(1): 64-70.
	WANG Y, YAO M L. The knowledge structure and diagnostic reasoning of medical experts[J]. Advances in Psychological Science, 2009, 17(1): 64-70.
[97]	KROMPA D, BAIER S, TRESP V. Type-constrained repres-entation learning in knowledge graphs[C]// Proceedings of the 14th International Conference on the Semantic Web Conference, Bethlehem, Oct 11-15, 2015: 640-655.
[98]	CHANG K W, YIH W T, YANG B S, et al. Typed tensor decomposition of knowledge bases for relation extraction[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Oct 25-29, 2014. Stroudsburg: ACL, 2014: 1568-1579.
[99]	WANG H L, ZHANG Q P, YUAN J H. Semantically enhanced medical information retrieval system: a tensor factorization based approach[J]. IEEE Access, 2017, 5: 7584-7593. DOI URL
[100]	BORDES A, USUNIER N, GARCÍA-DURÁN A, et al. Tran-slating embeddings for modeling multi-relational data[C]// Advances in Neural Information Processing Systems 26, Lake Tahoe, Dec 5-8, 2013. Red Hook: Curran Associates, 2013: 2787-2795.
[101]	WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Jul 27-31, 2014. Menlo Park: AAAI, 2014: 1112-1119.
[102]	JI G L, HE S Z, XU L H, et al. Knowledge graph embedding via dynamic mapping matrix[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Lin-guistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Beijing, Jul 26-31, 2015. Stroudsburg: ACL, 2015: 687-696.
[103]	XIAO H, HUANG M L, ZHU X Y. TransG: a generative model for knowledge graph embedding[C]// Proceedings of the 54th Annual Meeting of the Association for Computa-tional Linguistics, Berlin, Aug 7-12, 2016. Stroudsburg: ACL, 2016: 2316-2325.
[104]	HE S Z, LIU K, JI G L, et al. Learning to represent know-ledge graphs with Gaussian embedding[C]// Proceedings of the 24th ACM International Conference on Information and Knowledge Management, Melbourne, Oct 19-23, 2015. New York: ACM, 2015: 623-632.
[105]	李青青. 生物医学实体关系抽取算法与应用研究[D]. 大连: 大连理工大学, 2019.
	LI Q Q. Research on biomedical entity relation extraction algorithm and application[D]. Dalian: Dalian University of Technology, 2019.
[106]	陈德华, 殷苏娜, 乐嘉锦. 一种面向临床领域时序知识图谱的链接预测模型[J]. 计算机研究与发展, 2017, 54(12): 2687-2697.
	CHEN D H, YIN S N, LE J J. A link prediction model for clinical temporal knowledge graph[J]. Journal of Computer Research and Development, 2017, 54(12): 2687-2697.
[107]	官赛萍, 靳小龙, 贾岩涛, 等. 面向知识图谱的知识推理研究进展[J]. 软件学报, 2018, 29(10): 2966-2994.
	GUAN S P, JIN X L, JIA Y T, et al. Knowledge reasoning over knowledge graph: a survey[J]. Journal of Software, 2018, 29(10): 2966-2994.
[108]	SEO S, OH B, LEE K H. Reliable knowledge graph path representation learning[J]. IEEE Access, 2020, 8: 32816-32825. DOI URL
[109]	刘峤, 韩明皓, 杨晓慧, 等. 基于表示学习和语义要素感知的关系推理算法[J]. 计算机研究与发展, 2017, 54(8): 1682-1692.
	LIU Q, HAN M H, YANG X H, et al. Representation learning based relational inference algorithm with semantical aspect awareness[J]. Journal of Computer Research and Development, 2017, 54(8): 1682-1692.
[110]	郑子强. 面向慢性肾脏病中医医案的知识图谱学习与推理研究[D]. 成都: 电子科技大学, 2020.
	ZHENG Z Q. Research on knowledge graph learning and reasoning for TCM prescription of prescription of chronic kidney disease[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
[111]	DAS K, NEELAKANTAN A, BELANGER D. Chains of reasoning over entities, relations, and text using recurrent neural networks[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computa-tional Linguistics, Valencia, Apr 3-7, 2017. Stroudsburg: ACL, 2017: 132-141.
[112]	ARVIND N, BENJAMIN R, ANDREW M. Compositional vector space models for knowledge base completion[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural Language Processing, Beijing, Jul 26-28, 2015. Stroudsburg: ACL, 2015: 156-166.
[113]	XIE R B, LIU Z Y, JIA J, et al. Representation learning of knowledge graphs with entity descriptions[C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, Feb 12-17, 2016. Menlo Park: AAAI, 2016: 2659-2665.
[114]	CHEN W H, XIONG W H, YAN X F, et al. Variational know-ledge graph reasoning[C]// Proceedings of the 2018 Con-ference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno-logies, New Orleans, Jun 4-9, 2018. Stroudsburg: ACL, 2018: 1823-1832.
[115]	WANG Q, HAO Y S, CAO J. ADRL: an attention-based deep reinforcement learning framework for knowledge graph reasoning[J]. Knowledge-Based Systems, 2020, 197: 105910. DOI URL
[116]	WANG H, LI S Y, PAN R, et al. Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforcement learning[C]// Proceedings of the 2019 Con-ference on Empirical Methods in Natural Language Pro-cessing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. Stroudsburg: ACL, 2019: 2623-2631.
[117]	XIONG W, HOANG T, WANG W Y. DeepPath: a reinforce-ment learning method for knowledge graph reasoning[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Sep 9-11, 2017. Stroudsburg: ACL, 2017: 564-573.
[118]	TIWARI P, ZHU H, PANDEY H M. DAPath: distance-aware knowledge graph reasoning based on deep reinforce-ment learning[J]. Neural Networks, 2021, 135: 1-12. DOI URL
[119]	SHEN Y L, CHEN J S, HUANG P S, et al. M-walk: learning to walk over graphs using Monte Carlo tree search[C]// Advances in Neural Information Processing Systems 31, Montreal, Dec 3-8, 2018. Red Hook: Curran Associates, 2018: 6787-6798.
[120]	WANG Q, JI Y, HAO Y. GRL: knowledge graph completion with GAN-based reinforcement learning[J]. Knowledge-Based Systems, 2020, 209: 106421. DOI URL
[121]	WANG Q, HAO Y S. ALSTM: an attention-based long short-term memory framework for knowledge base reasoning[J]. Neurocomputing, 2020, 399: 342-351. DOI URL
[122]	殷苏娜. 糖尿病知识图谱链接预测方法的研究与应用[D]. 上海: 东华大学, 2018.
	YIN S N. Research and application on diabetes knowledge graph like prediction[D]. Shanghai: Donghua University, 2018.
[123]	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.
[124]	YANG B, HAN T, KIM Y. Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis[J]. Expert Systems with Application, 2004, 26(3): 387-395. DOI URL
[125]	SOCHER R, CHEN D, MANNING C, et al. Reasoning with neural tensor networks for knowledge base completion[C]// Advances in Neural Information Processing Systems 26, Lake Tahoe, Dec 5-10, 2013. Red Hook: Curran Ass-ociates, 2013: 926-934.
[126]	KAREGOWDA A, MANJUNATH A, JAYARAM M. Application of genetic algorithm optimized neural network connection weights for medical diagnosis of Pima Indians diabetes[J]. International Journal on Soft Computing, 2011, 2(2): 15-23. DOI URL
[127]	赵超. 面向中文文本的医学知识获取、表示与推理[D]. 哈尔滨: 哈尔滨工业大学, 2018.
	ZHAO C. Chinese text-oriented medical knowledge acquis-ition, representation and reasoning[D]. Harbin: Harbin In-stitute of Technology, 2018.
[128]	罗计根. 面向中医领域知识图谱构建的关键技术研究及应用[D]. 南昌: 江西中医药大学, 2019.
	LUO J G. Research and application of key technologies for knowledge graph in traditional Chinese medicine[D]. Nanchang: Jiangxi University of Traditional Chinese Med-icine, 2019.
[129]	李敬华, 易小烈, 杨德利. 面向临床决策支持的中医脾胃病本体知识库构建研究[J]. 中国医学创新, 2014, 11(27): 121-125.
	LI J H, YI X L, YANG D L. Research for clinical decision support of spleen and stomach disease of TCM ontology knowledge base building[J]. Medical Innovation of China, 2014, 11(27): 121-125.
[130]	庄严, 李国良, 冯建华. 知识库实体对齐技术综述[J]. 计算机研究与发展, 2016, 53(1): 165-192.
	ZHUANG Y, LI G L, FENG J H. A survey on entity align-ment of knowledge base[J]. Journal of Computer Research and Development, 2016, 53(1): 165-192.
[131]	WANG Y, YAO Q, KWOK J T, et al. Generalizing from a few examples: a survey on few-shot learning[J]. ACM Com-puting Surveys, 2020, 53(3): 1-34.
[132]	TAY Y, LUU A T, HUI S C. Non-parametric estimation of multiple embeddings for link prediction on dynamic know-ledge graphs[C]// Proceedings of the 31st Conference on Artificial Intelligence, San Francisco, Feb 4-9, 2017. Menlo Park: AAAI, 2017: 1243-1249.
[133]	刘勘, 张雅荃. 基于医疗知识图谱的并发症辅助诊断[J]. 中文信息学报, 2020, 34(10): 85-93.
	LIU K, ZHANG Y Q. Medical knowledge graph based auxiliary diagnosis of complications[J]. Journal of Chinese Information Processing, 2020, 34(10): 85-93.
[134]	ZHAO X, JIA Y, LI A, et al. Multi-source knowledge fusion: a survey[J]. World Wide Web, 2020, 23(4): 2567-2592. DOI URL
[135]	XIANG X Y, WANG Z R, JIA Y, et al. Knowledge graph-based clinical decision support system reasoning: a survey[C]// Proceedings of the IEEE 4th International Conference on Data Science in Cyberspace, Hangzhou, Jun 23-25, 2019. Red Hook: Curran Associates, 2019: 373-380.
[136]	CHEN X, JIA S, XIANG Y. A review: knowledge reaso-ning over knowledge graph[J]. Expert Systems with Applica-tions, 2020, 141: 112948.
[137]	丁雅琴. 基于知识图谱的医疗问答系统研究与开发[D]. 武汉: 华中师范大学, 2020.
	DING Y Q. Research and development of medical question answering system based on knowledge graph[D]. Wuhan: Central China Normal University, 2020.
[138]	曹明宇, 李青青, 杨志豪, 等. 基于知识图谱的原发性肝癌知识问答系统[J]. 中文信息学报, 2019, 33(6): 88-93.
	CAO M Y, LI Q Q, YANG Z H, et al. A question answering system for primary liver cancer based on knowledge graph[J]. Journal of Chinese Information Processing, 2019, 33(6): 88-93.

方法	优点	不足
基于医学词典和规则	根据医学词典和规则抽取,准确率高	依赖专家编写的规则和医学词典,难以适应数据不断变化的现实情况
基于机器学习方法	利用医学数据特点进行模型训练,相对简便,识别效率较高	需要高质量的数据进行训练,因而对人工标注数据的专业性要求较高
基于深度学习方法	无需专家制定复杂的抽取规则,降低了数据标注依赖,可利用海量未标注数据提升模型性能	准确率较差,需要人工进行抽取审核来进一步保证抽取质量

方法	优点	不足
基于医学词典和规则	根据医学词典和规则抽取,准确率高	依赖专家编写的规则和医学词典,难以适应数据不断变化的现实情况
基于机器学习方法	利用医学数据特点进行模型训练,相对简便,识别效率较高	需要高质量的数据进行训练,因而对人工标注数据的专业性要求较高
基于深度学习方法	无需专家制定复杂的抽取规则,降低了数据标注依赖,可利用海量未标注数据提升模型性能	准确率较差,需要人工进行抽取审核来进一步保证抽取质量

方法	目标
医学实体对齐	用于消除异构数据中的实体冲突,指向不一致问题,从而形成高质量知识
医学知识库融合	融合不同医学知识库以获得涵盖范围更为广泛和完整的医学知识图谱

方法	目标
医学实体对齐	用于消除异构数据中的实体冲突,指向不一致问题,从而形成高质量知识
医学知识库融合	融合不同医学知识库以获得涵盖范围更为广泛和完整的医学知识图谱

推理方式	推理概述	推理优势	推理弱势
基于逻辑规则的医学推理	基于一阶逻辑或谓词逻辑等逻辑规则进行推理	与人类的推理过程较为接近,逻辑推理能力强,并可利用人类的先验知识来进行辅助推理	对医学领域专家依赖性强,计算复杂度相对较高,泛化能力较差
基于表示学习的医学推理	主要是学习三元组中实体和关系的低维实值嵌入表示,然后基于此向量化的嵌入表示进行计算和推理	计算方便,可以充分利用知识图谱中的结构信息。对于大规模医学知识图谱的学习和推理效果较好	建模时不能引入先验知识来实现推理,对多跳推理效果较差
基于深度学习的医学推理	基于深度学习模型的优势对实体和关系进行学习,然后通过知识图谱中的结构信息和路径信息建立特征预测模型进行相关推理	无需数据标注,强大的特征捕捉能力,路径序列搜索在一定程度上提高了医学推理的可解释性	模型难以训练,长路径推理效果较差

Research and Development of Medical Knowledge Graph Reasoning

医学知识推理研究现状与发展

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 138

Related Articles 15

Recommended Articles

Metrics

[1]	YU Huilin, CHEN Wei, WANG Qi, GAO Jianwei, WAN Huaiyu. Knowledge Graph Link Prediction Based on Subgraph Reasoning [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1800-1808.
[2]	SA Rina, LI Yanling, LIN Min. Survey of Question Answering Based on Knowledge Graph Reasoning [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1727-1741.
[3]	TIAN Xuan, CHEN Hangxue. Survey on Applications of Knowledge Graph Embedding in Recommendation Tasks [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1681-1705.
[4]	HAN Yi, QIAO Linbo, LI Dongsheng, LIAO Xiangke. Review of Knowledge-Enhanced Pre-trained Language Models [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(7): 1439-1461.
[5]	WANG Baoliang, PAN Wencai. Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1354-1361.
[6]	GUO Xiaowang, XIA Hongbin, LIU Yuan. Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1343-1353.
[7]	ZHANG Zichen, YUE Kun, QI Zhiwei, DUAN Liang. Incremental Construction of Time-Series Knowledge Graph [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(3): 598-607.
[8]	XUE Dongqian, ZHAI Yanhui, ZHANG Shaoxia, LI Deyu, XU Weihua. Research of Inference Rules on Decision Implication and Variable Decision Implication [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2357-2364.
[9]	FAN Yuanyuan, LI Zhongmin. Research and Application Progress of Chinese Medical Knowledge Graph [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2219-2233.
[10]	LI Xiang, YANG Xingyao, YU Jiong, QIAN Yurong, ZHENG Jie. Double End Knowledge Graph Convolutional Networks for Recommender Systems [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 176-184.
[11]	WU Jiawei, SUN Yanchun. Recommendation System for Medical Consultation Integrating Knowledge Graph and Deep Learning Methods [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8): 1432-1440.
[12]	GAO Yang, LIU Yuan. Recommendation Algorithm Combining Knowledge Graph and Short-Term Preferences [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 1133-1144.
[13]	SHU Shitai, LI Song, HAO Xiaohong, ZHANG Liping. Knowledge Graph Embedding Technology: A Review [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(11): 2048-2062.
[14]	CHEN Zirui, WANG Xin, WANG Lin, XU Dawei, JIA Yongzhe. Survey of Open-Domain Knowledge Graph Question Answering [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(10): 1843-1869.
[15]	LIN Qika, ZHANG Lingling, LIU Jun, ZHAO Tianzhe. Question-aware Graph Convolutional Network for Educational Knowledge Base Question Answering [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(10): 1880-1887.