Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (19): 37-52.DOI: 10.3778/j.issn.1002-8331.2204-0243
• Research Hotspots and Reviews • Previous Articles Next Articles
YUAN Jun, LIU Guozhu, LIANG Hongtao, LUO Qingcai
Online:
2022-10-01
Published:
2022-10-01
袁俊,刘国柱,梁宏涛,罗清彩
YUAN Jun, LIU Guozhu, LIANG Hongtao, LUO Qingcai. Summary of Research and Application of Knowledge Graphs in Risk Management Field of Commercial Banks[J]. Computer Engineering and Applications, 2022, 58(19): 37-52.
袁俊, 刘国柱, 梁宏涛, 罗清彩. 知识图谱在商业银行风控领域的研究与应用综述[J]. 计算机工程与应用, 2022, 58(19): 37-52.
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[1] CHANG C L,MCALEER M,WONG W K.Risk and financial management of COVID?19 in business,economics and finance[J].Journal of Risk and Financial Management,2020,13(5):102. [2] SINGHAL A.Introducing the knowledge graph:Things,not strings[EB/OL].(2012-05-16).http://googleblog.blog-spot.ie/2012/05/introducing-knowledgegraph-things-not.html. [3] BERNERS-LEE T,HENDLER J,LASSILA O.The semantic web[J].Scientific American,2001,284(5):34-43. [4] EHRLINGER L,W?? W.Towards a definition of knowl- edge graphs[J].Semantics(Posters,Demos,Success),2016,48(1/4):2. [5] CHRISTIAN B,JENS L,GEORGI K,et al.Dbpedia—A crystallization point for the web of data[J].Web Semantics:Science,Services and Agents on the World Wide Web,2009,7(3):154-165. [6] MAHDISOLTANI F,BIEGA J,SUCHANEK F.Yago3:A knowledge base from multilingual Wikipediaes[C]//Proceedings of the 7th Biennial Conference on Innovative Data Systems Research,2014. [7] VRANDE?I? D,KR?TZSCH M.Wikidata:A free collab- orative knowledgebase[J].Communications of the ACM,2014,57(10):78-85. [8] DONG X,GABRILOVICH E,HEITZ G,et al.Knowledge vault:A web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2014:601-610. [9] 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. [10] World Wide Web Consortium.A direct mapping of relational data to RDF[EB/OL].(2012-09-27).http://www.w3.org/TR/rdb-direct-mapping/. [11] World Wide Web Consortium.R2RML:RDB to RDF mapping language[EB/OL].(2012-09-27).http://www.w3.org/TR/r2rml/. [12] KABASAKAL O,MUTLU A.Named entity recognition in Turkish bank documents[J].Kocaeli Journal of Science and Engineering,2021,4(2):86-92. [13] RAU L F.Extracting company names from text[C]//Proceedings of the 7th IEEE Conference on Artificial Intelligence Application,1991:29-32. [14] FARMAKIOTOU D,KARKALETSIS V,KOUTSIAS J,et al.Rule-based named entity recognition for Greek financial texts[C]//Proceedings of the Workshop on Computational Lexicography and Multimedia Dictionaries(COMLEX 2000),2000:75-78. [15] XU Z,BURDICK D,RASCHID L.Exploiting lists of names for named entity identification of financial institutions from unstructured documents[J].arXiv:1602. 04427,2016. [16] BIKEL D M,MILLER S,SCHWARTZ R,et al.Nymble:A high-performance learning name-fined[C]//Proceedings of the 5th Conference on Applied Natural Language Processing,1997:194-201. [17] BORTHWICK A E.A maximum entropy approach to named entity recognition[M].New York:New York University,1999. [18] LAFFERTY J,MCCALLUM A,PEREIRA F C N.Conditional random fields:Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning,2001. [19] ALVARADO J C S,VERSPOOR K,BALDWIN T.Domain adaption of named entity recognition to support credit risk assessment[C]//Proceedings of the Australasian Lang-uage Technology Association Workshop,2015:84-90. [20] CHENG W,GUO K,JIANG T,et al.A knowledge graph based framework in relationship modelling and real-time monitoring of market participants[J].Journal of Physics(Conference Series),2020,1682(1):012026. [21] WANG Y,LI Y,WU T.Research on compliance supervision data analysis model based on mass chat records in the inter-bank market[C]//Proceedings of the IEEE 2nd International Conference on Big Data,Artificial Intelligence and Internet of Things Engineering(ICBAIE),2021:368-380. [22] ZHENG X,LI L,ZHANG W.An end-to-end hierarchical multi-task learning framework of sentiment analysis and key entity identification for online financial texts[C]//Proceedings of the IEEE 24th International Conference on Computer Supported Cooperative Work in Design(CSCWD),2021:1245-1250. [23] ZHAO L,LI L,ZHENG X,et al.A BERT based sentiment analysis and key entity detection approach for online financial texts[C]//Proceedings of the IEEE 24th International Conference on Computer Supported Cooperative Work in Design(CSCWD),2021:1233-1238. [24] 周倚文,何锦源,王震,等.基于跨篇章事件提取的案件串联方法、装置及相关组件:ZL2021111882570[P].2022-02-15. ZHOU Y W,HE J Y,WANG Z,et al.Case series method,device and related components based on cross-chapter event extraction:ZL2021111882570[P].2022-02-15. [25] CAO P,CHEN Y,LIU K,et al.Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing,2018:182-192. [26] WU H,LEI Q,ZHANG X,et al.Creating a large-scale financial news corpus for relation extraction[C]//Proceedings of the 3rd International Conference on Artificial Intelligence and Big Data(ICAIBD),2020:259-263. [27] YAMAMOTO A,MIYAMURA Y,NAKATA K,et al.Company relation extraction from web news articles for analyzing industry structure[C]//Proceedings of the IEEE 11th International Conference on Semantic Computing(ICSC),2017:89-92. [28] YAN C,FU X,WU W,et al.Neural network based relation extraction of enterprises in credit risk management[C]//Proceedings of the IEEE International Conference on Big Data and Smart Computing(BigComp),2019:1-6. [29] YANG N,SHI D,HUA Y.Bidirectional gated recurrent unit neural networks for relation extraction of Chinese enterprises[C]//Proceedings of the IEEE 4th Information Technology,Networking,Electronic and Automation Control Conference(ITNEC),2020:1539-1543. [30] 李梦霄,马方.实体关系抽取技术在银行风控中的应用方法:ZL2020107569803[P].2020-10-30. LI M X,MA F.Application method of entity relationship extraction technology in bank risk control:ZL2020107569803[P].2020-10-30. [31] 田鸥,刘志强,余雨竹.风险传导概率知识图谱生成方法、装置、设备及存储介质:ZL2021114326800[P].2022-02-15. TIAN O,LIU Z Q,YU Y Z.Method,device,equipment and storage medium for generating risk conduction probability knowledge graph:ZL2021114326800[P].2022-02-15. [32] 唐晓波,刘志源.金融领域文本序列标注与实体关系联合抽取研究[J].情报科学,2021,39(5):3-11. TANG X B,LIU Z Y.Research on text sequence tagging and joint extraction of entity and relation in financial field[J].Information Science,2021,39(5):3-11. [33] 杨美芳,杨波.融入互注意力的风险领域实体关系抽取研究[J/OL].小型微型计算机系统:1-16(2022-02-25)[2022-03-30].http://kns.cnki.net/kcms/detail/21.1106.tp.20220225. 1712.018.html. YANG M F,YANG B.Research on entity relationship extraction in risk domain integrating mutual attention[J/OL].Journal of Chinese Computer Systems:1-16(2022-02-25)[2022-03-30].http://kns.cnki.net/kcms/detail/21.1106. tp.20220225.1712.018.html. [34] 刘政昊,钱宇星,衣天龙,等.知识关联视角下金融证券知识图谱构建与相关股票发现[J].数据分析与知识发现,2022,6(2):184-201. LIU Z H,QIAN Y X,YI T L,et al.Constructing knowledge graph for financial securities and discovering related stocks with knowledge association[J].Data Analysis and Knowledge Discovery,2022,6(2):184-201. [35] ZUO Z,LOSTER M,KRESTEL R,et al.Uncovering business relation ships:Context-sensitive relation ship extraction for difficult relationship types[C]//Proceedings of LWDA,2017:271. [36] 陈远.银行信贷风险识别知识图谱构建方法、装置、计算机设备及计算机可读存储介质:ZL2021108431617[P]. 2021-07-26. CHEN Y.Bank credit risk identification knowledge map construction method,device,computer equipment and computer-readable storage medium:ZL2021108431617[P]. 2021-07-26. [37] 林廷懋,谢雨成,柯颖,等.风险事件分级方法及装置:ZL2020114877086[P].2021-03-30. LIN T M,XIE Y C,KE Y,et al.Risk event grading method and device:ZL2020114877086[P].2021-03-30. [38] 吴超荣,袁进威,陈宝山,等.一种信息推送方法、装置、电子设备及计算机可读介质:ZL2021110051870[P].2021-11-30. WU C R,YUAN J W,CHEN B S,et al.Information push method,device,electronic device and computer- readable medium:ZL2021110051870[P].2021-11-30. [39] 张世奇,马进,周夏冰,等.基于预训练语言模型的商品属性抽取[J].中文信息学报,2022,36(1):56-64. ZHANG S Q,MA J,ZHOU X B,et al.Pre-trained language models for product attribute extraction[J].Journal of Chinese Information Processing,2022,36(1):56-64. [40] MIAO R,ZHANG X,YAN H,et al.A dynamic financial knowledge graph based on reinforcement learning and transfer learning[C]//Proceedings of the IEEE International Conference on Big Data,2019:5370-5378. [41] SONG D,SCHILDER F,HERTZ S,et al.Building and querying an enterprise knowledge graph[J].IEEE Transactions on Services Computing,2017,12(3):356-369. [42] WANG Z,ZHANG X,HU Y.A semantic path based approach to match subgraphs from large financial knowledge graph[C]//Proceedings of the International Conference on Mathematics,Big Data Analysis and Simulation and Modelling(MBDASM 2019),2019:86-92. [43] DING W,CHAUDHRI V K,CHITTAR N,et al.JEL:Applying end-to-end neural entity linking in JPMorgan Chase[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2021:15301-15308. [44] SUZUMURA T,ZHOU Y,BARACALDO N,et al.Towards federated graph learning for collaborative financial crimes detection[J].arXiv:1909.12946,2019. [45] WINKLER W E.Methods for record linkage and Bayesian networks[R].Statistical Research Division,US Census Bureau,Washington,DC,2002. [46] TRISEDYA B D,QI J,ZHANG R.Entity alignment between knowledge graphs using attribute embeddings[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2019:297-304. [47] YANG K,LIU S,ZHAO J,et al.COTSAE:Co-training of structure and attribute embeddings for entity alignment[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2020:3025-3032. [48] 唐晓波,谭明亮,李诗轩,等.企业破产预测系统模型构建及实现研究[J].情报学报,2019,38(10):1051-1065. TANG X B,TAN M L,LI S X,et al.Research on construction and implementation of a corporate bankruptcy prediction system model[J].Journal of the China Society for Scientific and Technical Information,2019,38(10):1051-1065. [49] CERI S,GOTTLOB G,TANCA L.What you always wanted to know about Datalog(and never dared to ask)[J].IEEE Transactions on Knowledge and Data Engineering,1989,1(1):146-166. [50] BELLOMARINI L,BENEDETTO D,GOTTLOB G,et al.Vadalog:A modern architecture for automated reasoning with large knowledge graphs[J].Information Systems,2020,105:101528. [51] ATZENI P,BELLOMARINI L,IEZZI M,et al.Weaving enterprise knowledge graphs:The case of company ownership graphs[C]//Proceedings of EDBT,2020:555-566. [52] GALáRRAGA L,TEFLIOUDI C,HOSE K,et al.AMIE:Association rule mining under incomplete evidence in ontological knowledge bases[C]//Proceedings of the 22nd International Conference on World Wide Web,2013:413-422. [53] ZHANG R,MAO Y,ZHAO W.Knowledge graphs completion via probabilistic reasoning[J].Information Sciences,2020,521:144-159. [54] LAO N,MITCHELL T,COHEN W.Random walk inference and learning in a large scale knowledge base[C]//Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing,2011:529-539. [55] GARDNER M,MITCHELL T.Efficient and expressive knowledge base completion using subgraph feature extraction[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2015. [56] WU H,CHANG Y,LI J,et al.Financial fraud risk analysis based on audit information knowledge graph[J].Procedia Computer Science,2022,199:780-787. [57] ZHANG W Y,LIU Y G,JIANG L H,et al.The construction of a domain knowledge graph and its application in supply chain risk analysis[C]//Proceedings of the International Conference on e-Business Engineering,2019:464-478. [58] OUYANG X,HONG L,ZHANG L.Query associations over big financial knowledge graph[C]//Proceedings of the International Conference on Big Scientific Data Management,2018:199-211. [59] 黄炜,周骏,冯云青,等.知识图谱在商业银行风险管理中的应用[J].信息技术与标准化,2020(5):84-89. HUANG W,ZHOU J,FENG Y Q,et al.Application of knowledge graph in banking risk management[J].Information Technology & Standardization,2020(5):84-89. [60] 吕华揆,洪亮,马费成.金融股权知识图谱构建与应用[J].数据分析与知识发现,2020,4(5):27-37. LYU H K,HONG L,MA F C.Constructing knowledge graph for financial equities[J].Data Analysis and Knowledge Discovery,2020,4(5):27-37. [61] BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of the International Conference on Neural Information Processing Systems,2013:2787-2795. [62] WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2014. [63] LIN Y,LIU Z,SUN M,et al.Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence,2015. [64] JI G,HE S,XU L,et al.Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing,2015:687-696. [65] XIAO H,HUANG M,HAO Y,et al.TransA:An adaptive approach for knowledge graph embedding[J].arXiv:1509.05490,2015. [66] JI G,LIU K,HE S,et al.Knowledge graph completion with adaptive sparse transfer matrix[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence,2016. [67] XIAO H,HUANG M,ZHU X.TransG:A generative mixture model for knowledge graph embedding[J].arXiv:1509.05488,2015. [68] MA C,SUN H Y,WANG S F,et al.Bond default prediction based on deep learning and knowledge graph technology[J].IEEE Access,2021,9:12750-12761. [69] BORDES A,GLOROT X,WESTON J,et al.A semantic matching energy function for learning with multi-relational data[J].Machine Learning,2014,94(2):233-259. [70] YANG B,YIH S W,HE X,et al.Embedding entities and relations for learning and inference in knowledge bases[C]//Proceedings of the International Conference on Learning Representations,2015. [71] ZHOU X,ZHU Q,LIU P,et al.Learning knowledge embeddings by combining limit-based scoring loss[C]//Proceedings of the 2017 ACM Conference on Information and Knowledge Management,2017:1009-1018. [72] NICKEL M,ROSASCO L,POGGIO T.Holographic em beddings of knowledge graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2016. [73] BENGIO Y,DUCHARME R,VINCENT P,et al.A neural probabilistic language model[J].The Journal of Machine Learning Research,2003,3:1137-1155. [74] DETTMERS T,MINERVINI P,STENETORP P,et al.Convolutional 2D knowledge graph embeddings[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2018. [75] JIANG X,WANG Q,WANG B.Adaptive convolution for multi-relational learning[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,2019:978-987. [76] VASHISHTH S,SANYAL S,NITIN V,et al.Interacte:Improving convolution-based knowledge graph embeddings by increasing feature interactions[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2020:3009-3016. [77] SHANG C,TANG Y,HUANG J,et al.End-to-end structure-aware convolutional networks for knowledge base completion[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2019:3060-3067. [78] ALAM M N,ALI M M.Loan default risk prediction using knowledge graph[C]//Proceedings of the 14th International Conference on Knowledge and Smart Technology(KST),2022:34-39. [79] XIONG W,HOANG T,WANG W Y.DeepPath:A reinforcement learning method for knowledge graph reasoning[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing,2017:564-573. [80] DAS R,DHULIAWALA S,ZAHEER M,et al.Go for a walk and arrive at the answer:Reasoning over paths in knowledge bases using reinforcement learning[C]//Proceedings of the International Conference on Learning Representations,2018. [81] LIN X V,SOCHER R,XIONG C.Multi-Hop knowledge graph reasoning with reward shaping[J].arXiv:1808. 10568,2018. [82] WANG H,LI S,PAN R,et al.Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforcement learning[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP),2019:2623-2631. [83] 熊盛武,陈小英,王盛,等.一种基于知识图谱的区域产业关联效应趋势预测方法:ZL2019113258527[P].2020-05-05. XIONG S W,CHEN X Y,WANG S,et al.Regional industry association effect trend prediction method based on knowledge graph:ZL2019113258527[P].2020-05-05. [84] SHUMOVSKAIA V,FEDYANIN K,SUKHAREV I,et al.Linking bank clients using graph neural networks powered by rich transactional data[C]//Proceedings of the IEEE 7th International Conference on Data Science and Advanced Analytics,2020:787-788. [85] CHENG D,WANG X,ZHANG Y,et al.Risk guarantee prediction in networked-loans[C]//Proceedings of the IJCAI International Joint Conference on Artificial Intelligence,2020. [86] WANG D,ZHANG Z,ZHOU J,et al.Temporal-aware graph neural network for credit risk prediction[C]//Proceedings of the 2021 SIAM International Conference on Data Mining(SDM),2021:702-710. [87] YANG B,LIAO Y.Research on enterprise risk knowledge graph based on multi-source data fusion[J].Neural Computing and Applications,2022,34(4):2569-2582. [88] SHAO D,ANNAM R.Translation embeddings for knowledge graph completion in consumer banking sector[C]//Proceedings of the International Joint Conference on Artificial Intelligence,2019:5-17. [89] 丁平,李帅.基于知识图谱的风险企业确定方法及装置:ZL2020108379562[P].2020-11-20. LI P,ZHANG S.Risk enterprise determination method and device based on knowledge graph:ZL2020108379562[P]. 2020-11-20. [90] 中国农业银行股份有限公司.风险预警方法及装置:ZL202111169796X[P].2022-01-07. Agricultural Bank of China Limited.Credit risk early warning method and system based on knowledge graph:ZL202111169796X[P].2022-01-07. [91] 杨奥.一种基于知识图谱的风险管控方法及装置:ZL2020110451385[P].2020-12-22. YANG A.Risk management and control method and device based on knowledge graph:ZL2020110451385[P]. 2020-12-22. [92] 孙杰,郭运雷.一种企业信贷风险的识别方法和装置:ZL2021102894719[P].2021-05-25. SUN J,GUO Y L.Enterprise credit risk identification method and device:ZL2021102894719[P].2021-05-25. [93] 金磐石,万光明,沈丽忠.基于知识图谱的小微企业贷款申请反欺诈方案[J].大数据,2019,5(4):100-112. JIN P S,WAN G M,SHEN L Z.Knowledge graph-based fraud detection for small and micro enterprise loans[J].Big Data Research,2019,5(4):100-112. [94] YANG K,XU W.FraudMemory:Explainable memory- enhanced sequential neural networks for financial fraud detection[C]//Proceedings of the 52nd Hawaii International Conference on System Sciences,2019. [95] MAO X,SUN H,ZHU X,et al.Financial fraud detection using the related-party transaction knowledge graph[J].Procedia Computer Science,2022,199:733-740. [96] BELLOMARINI L,LAURENZA E,SALLINGER E.Rule-based anti-money laundering in financial intelligence units:Experience and vision[C]//Proceedings of the Declarative AI 2020 Conference,2020. [97] XUE C.The application of knowledge graph technology in commercial bank customer association risk early warning system[C]//Proceedings of the 5th International Conference on Mechanical,Control and Computer Engineering(ICMCCE),2020:1083-1086. [98] WANG Z,GUO M,LI Z,et al.Knowledge graph construction for payment data risk control[M]//Innovative computing.Singapore:Springer,2020:1901-1907. [99] 蒋秀才,王佳.数字技术开辟普惠金融新路径[J].中国金融,2022(2):62-63. JIANG X C,WANG J.Digital technology opens up a new path of Inclusive Finance[J].China Finance,2022(2):62-63. [100] NOY N,GAO Y,JAIN A,et al.Industry-scale knowledge graphs:Lessons and challenges[J].Communications of the ACM,2019,62(8):36-43. [101] XIE Q,MA X,DAI Z,et al.An interpretable knowledge transfer model for knowledge base completion[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics,2017:950-962. [102] KAZEMI S M,POOLE D.Simple embedding for link prediction in knowledge graphs[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems,2018:4289-4300. |
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