Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)

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Research on the Optimal Promotion Path of Government Procurement Suppliers Based on Knowledge Graphs

Authors
Shuming Jiang1, Xingchao Lu1, *, Hong Zhang1
1Department of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
*Corresponding author. Email: luxingchao2021@163.com
Corresponding Author
Xingchao Lu
Available Online 27 October 2023.
DOI
10.2991/978-94-6463-276-7_20How to use a DOI?
Keywords
government procurement contract financing; service promotion; relational knowledge graph; Neo4j; optimal paths
Abstract

Government procurement is an important form of support for enterprise development. In China, there are a large number of suppliers involved in government procurement, and the transaction data is large and diverse. For platforms that provide contract financing services to suppliers in government procurement, how to utilize this data to better achieve the promotion of target customers is an important issue. Knowledge graph, as a technology for organizing and presenting knowledge, has advantages such as efficient knowledge organization and improved data quality. In order to solve the service promotion needs of the financing service platform, this paper pooled data from multiple parties and proposed a government procurement supplier relational knowledge graph construction method after processing and analyzing the data. Furthermore, this paper also designs the optimal promotion path for the financing service platform to reach the target suppliers on the basis of the constructed knowledge graph. The experimental results prove that the optimal promotion path promotion method proposed in this paper has a greater improvement in the application effect than the original direct contact promotion method of the platform.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
27 October 2023
ISBN
10.2991/978-94-6463-276-7_20
ISSN
2667-128X
DOI
10.2991/978-94-6463-276-7_20How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shuming Jiang
AU  - Xingchao Lu
AU  - Hong Zhang
PY  - 2023
DA  - 2023/10/27
TI  - Research on the Optimal Promotion Path of Government Procurement Suppliers Based on Knowledge Graphs
BT  - Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)
PB  - Atlantis Press
SP  - 178
EP  - 189
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-276-7_20
DO  - 10.2991/978-94-6463-276-7_20
ID  - Jiang2023
ER  -