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An Application of Knowledge Graph for Enterprise Risk Prediction

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Proceedings of the 12th International Conference on Computer Engineering and Networks (CENet 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 961))

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Abstract

The continuous development of artificial intelligence has brought new ideas to enterprise management. Knowledge graph is a special way of data storage and presentation. In recent years, it has received more and more attention, and the application scenario and scope are also expanding. This paper proposes an enterprise risk prediction method based on knowledge graph, which extracts and analyzes the internal business data and real-time news of the enterprise through knowledge graph, so as to realize the risk discovery and early warning analysis of the enterprise itself and related enterprises. It is a typical application of knowledge graph in the field of enterprise risk early warning analysis.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

    Pengfei Li

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  1. Pengfei Li
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Correspondence to Pengfei Li .

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Editors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Qi Liu

  2. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Xiaodong Liu

  3. School of Computer Science and Technology, Hainan University, Haikou, Hainan, China

    Jieren Cheng

  4. School of Electrical Engineering, University of Jinan, Jinan, Shandong, China

    Tao Shen

  5. School of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China

    Yuan Tian

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Li, P. (2022). An Application of Knowledge Graph for Enterprise Risk Prediction. In: Liu, Q., Liu, X., Cheng, J., Shen, T., Tian, Y. (eds) Proceedings of the 12th International Conference on Computer Engineering and Networks. CENet 2022. Lecture Notes in Electrical Engineering, vol 961. Springer, Singapore. https://doi.org/10.1007/978-981-19-6901-0_106

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  • DOI: https://doi.org/10.1007/978-981-19-6901-0_106

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6900-3

  • Online ISBN: 978-981-19-6901-0

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