基于知识图谱技术的上市企业产业链风险预测
网络安全与数据治理 9期
董士豪，郑朗，王特，于晓娟，王耀君
（中国农业大学信息与电气工程学院，北京100038）
摘要： 随着产业互联网的飞速发展，面对海量的产业数据，构建知识图谱等自然语言处理应用需求逐渐增长。产业信息的有效管理和挖掘有助于及时发现所面临的风险和机遇，产业链风险预测可以为监管部门提供产业风险预警手段。针对以上问题，本文以知识图谱相关知识为科学依据，提出了基于知识图谱技术的产业文本数据实体标注准则，对海量上市公司产业信息进行知识抽取，形成自上而下的三维产业知识图谱。同时研究了上市企业产业知识图谱特定产业链知识的内在联系，总结规律并结合产业链往年时序图特征信息实现图谱推理，成功的对产业链中上市企业市值等信息进行了预测和分析。
中图分类号：F830
文献标识码：A
DOI:10.19358/j.issn.2097-1788.2023.09.004
引用格式：董士豪，郑朗，王特,等.基于知识图谱技术的上市企业产业链风险预测［J］.网络安全与数据治理，2023，42（9）：21-28.
文献标识码：A
DOI:10.19358/j.issn.2097-1788.2023.09.004
引用格式：董士豪，郑朗，王特,等.基于知识图谱技术的上市企业产业链风险预测［J］.网络安全与数据治理，2023，42（9）：21-28.
Risk prediction of the industrial chain of listed enterprises based on knowledge graph technology
Dong Shihao，Zheng Lang，Wang Te，Yu Xiaojuan，Wang Yaojun
(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)
Abstract： With the rapid development of the industrial Internet, the demand for natural language processing applications such as building knowledge graphs is gradually increasing in the face of massive industrial data. The effective management and mining of industrial information can help to discover the risks and opportunities faced in time, and the risk prediction of the industrial chain can provide regulatory authorities with early warning means for industrial risks. In view of the above problems, this paper takes the knowledge related to knowledge graph as the scientific basis, and puts forward the criteria for labeling industrial text data entities based on knowledge graph technology, extracts knowledge from massive listed companies′ industrial information, and forms a topdown threedimensional industrial knowledge map. At the same time, the intrinsic relationship of specific industrial chain knowledge of listed enterprises in the industrial knowledge graph is studied, the law is summarized, and the graph reasoning is realized by combining the characteristic information of the time series chart of the industrial chain in previous years, and the market value of listed enterprises in the industrial chain is successfully predicted and analyzed
Key words : knowledge graph; industry chain analysis; risk prediction; entity relationship callouts
0 引言
产业知识图谱是结构化的产业语义知识库，通过形式化描述产业领域的概念、实体、属性及其相互关系，使概念、实体间相互联结，构成网状知识结构。产业涉及范围广泛，本研究以产业大类中的上市企业、基金、上市企业业务链、产业链、基金经理和股东等为研究对象，形成了知识覆盖面广、数据更新实时、精准度高的自上到下的三维度产业知识图谱。根据中国产业经济信息网和中国证券业协会规定的18大类产业为第一维度知识；以上市企业、基金、基金经理和股东组成的第二维度知识；再到第三维度的公司业务链知识，最终完成了产业知识图谱的构建。根据研究目标及思路，下文确定了数据获取方向和主要的获取方法。
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作者信息：
董士豪，郑朗，王特，于晓娟，王耀君
（中国农业大学信息与电气工程学院，北京100038）
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