Please wait a minute...
Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (9): 39-50    DOI: 10.11925/infotech.2096-3467.2022.0921
Current Issue | Archive | Adv Search |
Predicting Dynamic Relationship for Financial Knowledge Graph
Zhang Zhijian(),Ni Zhenni,Liu Zhenghao,Xia Sudi
Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
School of Information Management, Wuhan University, Wuhan 430072, China
Big Data Institute, Wuhan University, Wuhan 430072, China
Download: PDF (1302 KB)   HTML ( 13 ) 
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper proposes a data-driven prediction method for dynamic relationships, aiming to provide a new perspective for rapidly updating the financial knowledge graph. [Methods] First, we regularly crawled relevant information from the Internet according to the monitoring list. Then, we used the Mask Language Model to construct a dataset and train the model. Third, we extracted the hierarchical structure of the financial knowledge graph to build a hidden layer of the neural network. The neurons contained in the hidden layer represent named entities. Fourth, we connected the hidden layers by a relationship matrix and predicted the dynamic relationships by updating the connection matrix. [Results] We examined the proposed model with the two equity changes at the beginning of the “Baowan” event. Our new model quickly captured the changes in the relationship between corresponding entities of the financial graph in different periods. [Limitations] Due to the characteristics of unsupervised learning, the predicted relationship is relatively divergent, which requires manual calibration verification. [Conclusions] With sufficient data, the proposed method can effectively capture the changes in the relationship between entities without manual annotation. It will effectively and continuously predict the relationship of the financial knowledge graph.
Key words： Knowledge Graph      Relationship Prediction      Self-Supervised Learning     
Received: 31 August 2022      Published: 24 October 2023
ZTFLH:  G256  
Fund:The National Natural Science Foundation of China(91646206);The Scientific and Technological Innovation 2030 - “New Generation Artificial Intelligence”(2020AAA0108505)
Corresponding Authors: Zhang Zhijian，ORCID：0000-0002-7758-9277，E-mail：zzjian@whu.edu.cn。   
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Zhijian Zhang
Zhenni Ni
Zhenghao Liu
Sudi Xia

Cite this article:

Zhang Zhijian, Ni Zhenni, Liu Zhenghao, Xia Sudi. Predicting Dynamic Relationship for Financial Knowledge Graph. Data Analysis and Knowledge Discovery, 2023, 7(9): 39-50.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0921     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I9/39

Dynamic Relationship Prediction Model Based on Financial Knowledge Graph
Financial Ontology Library
Fully Connected Structure
模型名称 参数名词 参数说明 参数值
TransE learning_rate 学习率 0.01
embedding_dim 生成知识向量的维度大小 128
margin 正负样本之间的距离 1
batch_size 每个训练批次的数据量 32
max_epochs 最大训练轮次 200
KGANN learning_rate 学习率 0.001
batch_size 每个训练批次的数据量 16
optimizer 优化器 Adam
dropout_ratio 随机使神经元停止运算的比例 0.4
max_length 句子的最大输入长度 120
max_epochs 最大训练轮次 200
Model Parameter Settings
股东顺序 股东名称 持股数 持股比例
1 华润股份有限公司 1 645 494 720 14.91%
2 HKSCC NOMINEES LIMITED 1 314 939 877 11.91%
3 国信证券-工商银行-国信金鹏分级1号集合资产管理计划 364 036 073 3.30%
4 安邦人寿保险股份有限公司-稳健型投资组合 234 552 728 2.13%
5 GIC PRIVATE LIMITED 145 335 765 1.32%
Introduction to Shareholders of Vanke
Proportion of Cosine Similarity Between Updated and Existing Relationship
[1] 王爱萍, 胡海峰. 新发展阶段我国金融风险的新特点、新挑战及防范对策[J]. 人文杂志, 2021(12): 99-108.
[1] (Wang Aiping, Hu Haifeng. New Characteristics, New Challenges of China’s Financial Risk and the Countermeasures in the New Development Stage[J]. The Journal of Humanities, 2021(12): 99-108.)
[2] 政府工作报告[EB/OL]. [2022-03-12]. https://www.gov.cn/gongbao/content/2023/content_5747260.htm.
[2] (The Government Work Report[EB/OL]. [2022-03-12]. https://www.gov.cn/gongbao/content/2023/content_5747260.htm.)
[3] 党印, 苗子清, 张涛, 等. 大数据方法在系统性金融风险监测预警中的应用进展[J]. 金融发展研究, 2022(2): 3-12.
[3] (Dang Yin, Miao Ziqing, Zhang Tao, et al. Application Progress of Big Data Methods in Systemic Financial Risk Monitoring and Early Warning[J]. Journal of Financial Development Research, 2022(2): 3-12.)
[4] 黄恒琪, 于娟, 廖晓, 等. 知识图谱研究综述[J]. 计算机系统应用, 2019, 28(6): 1-12.
[4] (Huang Hengqi, Yu Juan, Liao Xiao, et al. Review on Knowledge Graphs[J]. Computer Systems & Applications, 2019, 28(6): 1-12.)
[5] Ji S X, Pan S R, Cambria E, et al. A Survey on Knowledge Graphs: Representation, Acquisition, and Applications[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 494-514.
doi: 10.1109/TNNLS.2021.3070843
[6] 张宁豫, 陈曦, 陈矫彦, 等. 基于位置的知识图谱链接预测[J]. 中文信息学报, 2018, 32(4): 80-86.
[6] (Zhang Ningyu, Chen Xi, Chen Jiaoyan, et al. Location Based Link Prediction for Knowledge Graph[J]. Journal of Chinese Information Processing, 2018, 32(4): 80-86.)
[7] Nayyeri M, Cil G M, Vahdati S, et al. Trans4E: Link Prediction on Scholarly Knowledge Graphs[J]. Neurocomputing, 2021, 461: 530-542.
doi: 10.1016/j.neucom.2021.02.100
[8] 李纲, 王施运, 毛进, 等. 面向态势感知的国家安全事件图谱构建研究[J]. 情报学报, 2021, 40(11): 1164-1175.
[8] (Li Gang, Wang Shiyun, Mao Jin, et al. Construction of National Security Event Map and Its Application for Situation Awareness[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(11): 1164-1175.)
[9] 陶玥, 余丽, 吴振新. CoTransH: 科技文献知识图谱中语义关系预测的翻译模型[J]. 情报理论与实践, 2021, 44(11): 187-196.
doi: 10.16353/j.cnki.1000-7490.2021.11.025
[9] (Tao Yue, Yu Li, Wu Zhenxin. CoTransH: A Translation Model for Semantic Relation Prediction in the Knowledge Graph of Scientific Articles[J]. Information Studies: Theory & Application, 2021, 44(11): 187-196.)
doi: 10.16353/j.cnki.1000-7490.2021.11.025
[10] 余传明, 张贞港, 孔令格. 面向链接预测的知识图谱表示模型对比研究[J]. 数据分析与知识发现, 2021, 5(11): 29-44.
[10] (Yu Chuanming, Zhang Zhengang, Kong Lingge. Comparing Knowledge Graph Representation Models for Link Prediction[J]. Data Analysis and Knowledge Discovery, 2021, 5(11): 29-44.)
[11] 张志剑, 刘政昊, 马费成. 面向互联网舆情事件的企业风险识别——基于KGANN模型[J]. 工程管理科技前沿, 2022, 41(1): 65-73.
[11] (Zhang Zhijian, Liu Zhenghao, Ma Feicheng. Enterprise Risk Identification for Internet Public Opinion Events—Based on KGANN Model[J]. Frontiers of Science and Technology of Engineering Management, 2022, 41(1): 65-73.)
[12] Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810.04805.
[13] Keshavarzi A, Kannan N, Kochut K. RegPattern2Vec: Link Prediction in Knowledge Graphs[C]// Proceedings of the 2021 IEEE International IOT, Electronics and Mechatronics Conference. IEEE, 2021: 1-7.
[14] Richards B L, Mooney R J. First-Order Theory Revision[A]//BirnbaumL A, CollinsG C. Machine Learning Proceedings 1991[M]. Amsterdam: Elsevier, 1991: 447-451.
[15] Galárraga L A, 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. New York: ACM, 2013: 413-422.
[16] Mitchell T, Fredkin E. Never-Ending Language Learning[C]// Proceedings of the IEEE International Conference on Big Data. IEEE, 2015.
[17] Richardson M, Domingos P. Markov Logic: A Unifying Framework for Statistical Relational Learning[C]// Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and Its Connections to Other Fields. 2004:339-371.
[18] 封皓君, 段立, 张碧莹. 面向知识图谱的知识推理综述[J]. 计算机系统应用, 2021, 30(10): 21-30.
[18] (Feng Haojun, Duan Li, Zhang Biying. Overview on Knowledge Reasoning for Knowledge Graph[J]. Computer Systems & Applications, 2021, 30(10): 21-30.)
[19] Lao N, Cohen W W. Relational Retrieval Using a Combination of Path-Constrained Random Walks[J]. Machine Learning, 2010, 81(1): 53-67.
doi: 10.1007/s10994-010-5205-8
[20] Gardner M, Talukdar P, Krishnamurthy J, et al. Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. PA, USA: ACL, 2014: 397-406.
[21] Xiong W H, 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. PA, USA: ACL, 2017: 564-573.
[22] 林泽斐, 欧石燕. 融合结构与文本特征的知识图谱关系预测方法研究[J]. 图书情报工作, 2020, 64(21): 99-110.
doi: 10.13266/j.issn.0252-3116.2020.21.013
[22] (Lin Zefei, Ou Shiyan. Research on Relation Prediction in Knowledge Graphs by Fusing Structure and Text Features[J]. Library and Information Service, 2020, 64(21): 99-110.)
doi: 10.13266/j.issn.0252-3116.2020.21.013
[23] Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling Relational Data with Graph Convolutional Networks[C]// Proceedings of the 15th European Semantic Web Conference. Cham: Springer, 2018: 593-607.
[24] Nathani D, Chauhan J, Sharma C, et al. Learning Attention-Based Embeddings for Relation Prediction in Knowledge Graphs[OL]. arXiv Preprint, arXiv: 1906.01195.
[25] Li M L, Jia Y T, Wang Y Z, et al. Hierarchy-Based Link Prediction in Knowledge Graphs[C]// Proceedings of the 25th International Conference Companion on World Wide Web. New York: ACM, 2016: 77-78.
[26] Bordes A, Usunier N, Garcia-Durán A, et al. Translating Embeddings for Modeling Multi-Relational Data[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. New York: ACM, 2013: 2787-2795.
[27] 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. New York: ACM, 2014: 1112-1119.
[28] 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 Intelligence. New York: ACM, 2015: 2181-2187.
[29] 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. New York: ACM, 2011: 809-816.
[30] Liu H X, Wu Y X, Yang Y M. Analogical Inference for Multi-Relational Embeddings[C]// Proceedings of the 34th International Conference on Machine Learning. 2017: 2168-2178.
[31] Yang B S, Yih W T, He X D, et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases[OL]. arXiv Preprint, arXiv: 1412.6575.
[32] Nickel M, Rosasco L, Poggio T. Holographic Embeddings of Knowledge Graphs[C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. New York: ACM, 2016: 1955-1961.
[33] Trouillon T, Welbl J, Riedel S, et al. Complex Embeddings for Simple Link Prediction[C]// Proceedings of the 33rd International Conference on Machine Learning. New York: ACM, 2016: 2071-2080.
[34] 国家统计局. 《统计上大中小微型企业划分办法》[J]. 轻工标准与质量, 2011(5):24.
[34] (National Bureau of Statistics. Division Method of Large, Medium, Small and Micro Enterprise[J]. Standard & Quality of Light Industry, 2011(5):24.)
[35] 韩毅, 乔林波, 李东升, 等. 知识增强型预训练语言模型综述[J]. 计算机科学与探索, 2022, 16(7): 1439-1461.
doi: 10.3778/j.issn.1673-9418.2108105
[35] (Han Yi, Qiao Linbo, Li Dongsheng, et al. Review of Knowledge-Enhanced Pre-Trained Language Models[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(7): 1439-1461.)
doi: 10.3778/j.issn.1673-9418.2108105
[36] Rosenblatt F. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain[J]. Psychological Review, 1958, 65(6): 386-408.
doi: 10.1037/h0042519 pmid: 13602029
[37] 郑洁, 黄辉, 秦永彬. 一种融合法律知识的相似案例匹配模型[J]. 数据分析与知识发现, 2022, 6(7): 99-106.
[37] (Zheng Jie, Huang Hui, Qin Yongbin. Matching Similar Cases with Legal Knowledge Fusion[J]. Data Analysis and Knowledge Discovery, 2022, 6(7): 99-106.)
[38] 刘长喜, 吴明星. 政治与市场的互构——宝万之争的并购逻辑转变研究[J]. 社会学评论, 2021, 9(1): 103-124.
[38] (Liu Changxi, Wu Mingxing. Mutual Construction of Politics and Market: A Research on the Transitional Logic of M & A Between Baoneng Group and Vanke[J]. Sociological Review of China, 2021, 9(1): 103-124.)
[39] 汤晓东, 郑博宏, 罗皓亮. “宝万之争”的脉络、焦点与研究目的[J]. 清华金融评论, 2016(S1): 27-35.
[39] (Tang Xiaodong, Zheng Bohong, Luo Haoliang. The Context, Focus and Research Purpose of “the Dispute Between Bao and Wan”[J]. Tsinghua Financial Review, 2016(S1): 27-35.)
[1] Zhai Dongsheng, Lou Ying, Kan Huimin, He Xijun, Liang Guoqiang, Ma Zifei. Constructing TCM Knowledge Graph with Multi-Source Heterogeneous Data[J]. 数据分析与知识发现, 2023, 7(9): 146-158.
[2] Pu Xianghe, Wang Hongbin, Xian Yantuan. Few-Shot Knowledge Graph Completion Combined with Type-Aware Attention[J]. 数据分析与知识发现, 2023, 7(9): 51-63.
[3] Wang Xiaofeng, Sun Yujie, Wang Huazhen, Zhang Hengzhang. Construction and Verification of Type-Controllable Question Generation Model Based on Deep Learning and Knowledge Graphs[J]. 数据分析与知识发现, 2023, 7(6): 26-37.
[4] Li Kaijun, Niu Zhendong, Shi Kaize, Qiu Ping. Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding[J]. 数据分析与知识发现, 2023, 7(5): 48-59.
[5] Wang Yinqiu, Yu Wei, Chen Junpeng. Automatic Question-Answering in Chinese Medical Q & A Community with Knowledge Graph[J]. 数据分析与知识发现, 2023, 7(3): 97-109.
[6] Du Yue, Chang Zhijun, Dong Mei, Qian Li, Wang Ying. Constructing Large-scale Knowledge Graph for Massive Sci-Tech Literature[J]. 数据分析与知识发现, 2023, 7(2): 141-150.
[7] Zhang Zhengang, Yu Chuanming. Knowledge Graph Completion Model Based on Entity and Relation Fusion[J]. 数据分析与知识发现, 2023, 7(2): 15-25.
[8] Peng Cheng, Zhang Chunxia, Zhang Xin, Guo Jingtao, Niu Zhendong. Reasoning Model for Temporal Knowledge Graph Based on Entity Multiple Unit Coding[J]. 数据分析与知识发现, 2023, 7(1): 138-149.
[9] Zhang Han, An Xinyu, Liu Chunhe. Building Multi-Source Semantic Knowledge Graph for Drug Repositioning[J]. 数据分析与知识发现, 2022, 6(7): 87-98.
[10] Liu Chunjiang, Li Shuying, Hu Hanlin, Fang Shu. Graph Databases for Complex Network Analysis[J]. 数据分析与知识发现, 2022, 6(7): 1-11.
[11] Liu Kan, Xu Qinya, Yu Lu. Constructing Knowledge Graph for Business Environment[J]. 数据分析与知识发现, 2022, 6(4): 82-96.
[12] Zhang Wei, Wang Hao, Chen Yuetong, Fan Tao, Deng Sanhong. Identifying Metaphors and Association of Chinese Idioms with Transfer Learning and Text Augmentation[J]. 数据分析与知识发现, 2022, 6(2/3): 167-183.
[13] Liu Zhenghao, Qian Yuxing, Yi Tianlong, Lv Huakui. Constructing Knowledge Graph for Financial Securities and Discovering Related Stocks with Knowledge Association[J]. 数据分析与知识发现, 2022, 6(2/3): 184-201.
[14] Cheng Zijia, Chen Chong. Question Comprehension and Answer Organization for Scientific Education of Epidemics[J]. 数据分析与知识发现, 2022, 6(2/3): 202-211.
[15] Hou Dang, Fu Xiangling, Gao Songfeng, Peng Lei, Wang Youjun, Song Meiqi. Mining Enterprise Associations with Knowledge Graph[J]. 数据分析与知识发现, 2022, 6(2/3): 212-221.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn