The Method of Constructing Knowledge Graph Based on the Trajectory of Counterfeit Cigarette and Wine Sales Data
- DOI
- 10.2991/978-94-6463-262-0_118How to use a DOI?
- Keywords
- Knowledge Graph; Trajectory of Counterfeit Cigarette and Wine; Neo4j
- Abstract
In recent years, knowledge graph has gradually developed from the traditional knowledge analysis application to the application of social entities and relationships. In this paper, the application of knowledge graph to sales data relationships is studied, and the data source is obtained by taking the sales data relationship related to counterfeit tobacco and alcohol as an example. We built the relationship between sales data related to fake cigarettes and alcohol through data pre-processing, data governance, population identification of fake cigarettes and alcohol, information extraction, etc. We used the Neo4j database for storage and built the knowledge graph of the relationship between sales data related to fake cigarettes and alcohol. The experimental verification shows that based on the real information of fake cigarettes and alcohol, we can achieve a visual display of sales data and relationship information.
- Copyright
- © 2024 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
TY - CONF AU - Chunjun Zheng AU - Ning Jia AU - Yingqiu Li AU - Yanxin Xu AU - Jianbo Zhou PY - 2023 DA - 2023/10/09 TI - The Method of Constructing Knowledge Graph Based on the Trajectory of Counterfeit Cigarette and Wine Sales Data BT - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023) PB - Atlantis Press SP - 1160 EP - 1168 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-262-0_118 DO - 10.2991/978-94-6463-262-0_118 ID - Zheng2023 ER -