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| Mining Enterprise Associations with Knowledge Graph |
Hou Dang1,3,4,Fu Xiangling1,3,4( ),Gao Songfeng2,Peng Lei2,Wang Youjun2,Song Meiqi1,3,4 |
1School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
2Huarong Rongtong(Beijing) Technology Co.,Ltd., Beijing 100033, China
3BUPT and Huarong Joint Lab of Smart Finance, Beijing 100876, China
4BUPT and Key Laboratory of Trustworthy Distributed Computing and Service, Beijing 100876, China |
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Abstract [Objective] This paper tries to explore relationships among enterprises in production and operation with the help of knowledge graph, aiming to provide new directions for risk management and valuation. [Context] In production and operation, there are enormous complex relationships containing valuable information. [Methods] We used the structured enterprise data tables to construct the enterprise knowledge graph, which helped us search the association between enterprises, and find the actual controller of enterprises and the affiliated groups. [Results] The constructed knowledge graph included more than 1.4 million entities, such as companies and individuals, and more than 3 million relationships on equity, guarantee, senior management, investment and so on. Based on the path and search algorithm of the graph, we found the association, actual controller and the affiliations. [Conclusions] The proposed algorithm could effectivley identify the hidden enterprise association relationship.
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Received: 31 August 2021
Published: 14 April 2022
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| Fund:National Natural Science Foundation of China(91546121) |
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Corresponding Authors:
Fu Xiangling,ORCID： 0000-0002-1492-2829
E-mail: fuxiangling@bupt.edu.cn
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