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
[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.
(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.)
(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.)
(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
(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
(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.)
(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
(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.)
(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.
(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.
(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.)
(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
(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.)
(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.)
