摘要
【目的】提出一种数据驱动的动态关系预测方法,为金融知识图谱的快速更新方法研究提供新视角。【方法】根据监测列表和检索策略在互联网爬取相关信息,使用掩码语言建模任务构建数据集并训练模型;提取金融知识图谱的层级结构搭建神经网络的隐藏层,隐藏层所含的神经元表示命名实体,隐藏层之间使用关系矩阵连接,通过对连接矩阵更新实现对关系的动态预测。【结果】以“宝万之争”事件初期的两次股权变更为例,本文方法可以在不同时期快速捕捉金融图谱中对应实体间关系的变化,验证了方法的有效性。【局限】受限于自监督学习的特性,所预测的关系较为发散,仍需人工进行校准核验。【结论】本文所提方法在数据充分的情况下,无需人工标注即可获取实体间关系的变化,可以对金融知识图谱的关系进行高效持续的预测。
[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.
作者
张志剑
倪珍妮
刘政昊
夏苏迪
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)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2023年第9期39-50,共12页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金重大研究计划重点支持项目(项目编号:91646206)
“科技创新2030-‘新一代人工智能’”重大项目(项目编号:2020AAA0108505)的研究成果之一。
关键词
知识图谱
关系预测
自监督学习
Knowledge Graph
Relationship Prediction
Self-Supervised Learning
