Research on the Construction of Three Level Customer Service Knowledge Graph in Electric Marketing

  • Zelin WangEmail author
  • Zhengqi Zhou
  • Muyan Zhou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)


With the advent of the information explosion era, more and more enterprises realize that knowledge management and application requirements are the key points that enterprises attach importance to. This paper solved existing problems by building customer service of power industry marketing and relevant theories of Knowledge Graph, and explored the three-level customer Knowledge Graph framework of power system marketing. Neo4j was used to realize knowledge reasoning, and relatively good results were obtained in practice.


Electricity marketing Customer service Knowledge graph Neo4j 



This work is supported by the National Natural Science Foundation of China (61171132, 61340037), the Natural Science Foundation of Nantong University (BK2014064), Nantong Research Institute for Advanced Communication Technologies (KFKT2016B06), Modern education technology research project of Jiangsu (2017-R-54131).


  1. 1.
    Kang, J.: Research on fine power marketing service management measures. Manag. Sci. 09, 24–25 (2017)Google Scholar
  2. 2.
    Wang, G.: The Internet _ power marketing intelligent interactive service construction. China Power Enterp. Manag. 01, 48–49 (2017)Google Scholar
  3. 3.
    Tian, X., Liu, Y., Wang, J., et al.: Application value of customer service knowledge graph construction of power grid corp. Shandong Electr. Power Technol. 42(12), 65–69 (2015)Google Scholar
  4. 4.
    Xu, C.: Analysis of power network marketing mode under smart grid. Commun. World 03, 219–220 (2017)Google Scholar
  5. 5.
    Liu, J., Yang, L., Duan, H., et al.: Overview of knowledge graph technology. Comput. Res. Develop. 53(3), 582–600 (2016)Google Scholar
  6. 6.
    Baidu next generation search engine prototype exposure application knowledge graphing technology. Comput. Program. Skills Maintenance 19, 4 (2013)Google Scholar
  7. 7.
  8. 8.
    Li, W., Xiao, Y., Wang, W.: Character entity recognition based on Chinese knowledge graph. Comput. Eng. 43(3), 226–234 (2017)Google Scholar
  9. 9.
    Ochs, C., Tian, T., Geller, J., et al.: Google knows who is famous today - building an ontology from search engine knowledge and DBpedia. In: Proceedings of the 5th IEEE International Conference on Semantic Computing, pp. 320–327. IEEE, Piscataway (2011)Google Scholar
  10. 10.
    Ma, L., Sun, Y., Liu, Q.: Ontology matching in semantic Web research. Comput. Appl. 34(5), 10–18 (2017)Google Scholar
  11. 11.
    Diallo, G.: An effective method of large scale ontology matching. J. Biomed. Semant. 5, 44–63 (2014)CrossRefGoogle Scholar
  12. 12.
    Liu, H., Jia, Y., Wang, Y., et al.: Knowledge base classification system matching method based on composite structure. Comput. Res. Dev. 54(1), 50–62 (2017)Google Scholar
  13. 13.
    Wang, R., Yang, Y., Yung, X.: Exploring the Chinese business knowledge graph based on depth learning and graph database. Books Inform. 01, 110–118 (2016)Google Scholar
  14. 14.
    Ma, Y., Wu, Z.: Modeling and analysis of large power data based on Neo4j. New Technol. Electr. Electr. Energy 35(2), 24–31 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyNantong UniversityNantongChina

Personalised recommendations