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A Method for Constructing a Knowledge Graph of Electric Power Digital Marketing Based on Artificial Intelligence Deep Learning

Published: 31 July 2024 Publication History

Abstract

With the rapid development of digitalization and informatization in the power industry, power companies have accumulated a large amount of data in various business fields. This article focuses on the intelligent application requirements in the field of electric power marketing, and designs and constructs a knowledge graph of electric power marketing business that includes domain background knowledge. Firstly, utilize the relationships between the basic business data tables organized by domain experts to construct a conceptual ontology. Next, through operations such as data cleaning, data filtering, and feature selection, traverse the data tables of the business database, use knowledge graph tools to obtain a knowledge graph, and finally use the power marketing knowledge graph to build an intelligent question answering application that supports natural language question answering services in the field of power marketing, better serving power users. Experimental results have shown that the power marketing knowledge graph constructed in this article, along with intelligent question answering applications, can accurately answer user questions and significantly improve user satisfaction.

References

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Sheng Fangzheng, Yu Jin, Wang Cong. Application of Artificial Intelligence Technology in Digital Service Scenarios of Electric Power Marketing [J]. Electrical Technology and Economics, 2023, (09): 125-127.
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Zhang Cambodia. Informatization of Power Marketing under the Background of Smart Grid [J]. Mold Manufacturing, 2023, 23 (10): 217-219.
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Hu Quangui, Xie Ke, Ren Lingling, Application Analysis of Artificial Intelligence in the Power Industry [J]. Power Information and Communication Technology, 2021, 19 (01): 73-80.
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Cao Lihui, Liao Xiaoyun. Analysis of Intelligent Transformation and Development Trends in Power Marketing Services [J]. Integrated Circuit Applications, 2023, 40 (09): 318-319.
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Xue Xiaoru, Xu Daolei, Lu Yu Research on Power Data Analysis Algorithms Based on Knowledge Graph and Artificial Intelligence [J]. Electronic Design Engineering, 2023, 31 (22): 139-143.
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Zhang Zebin, Wang Fei, Zhang Anling, Review of Research and Application of Knowledge Graph in the Field of Power Fault Diagnosis [J]. Industrial Control Computer, 2023, 36 (10): 150-152.
[7]
Li Senyan. Research on Low Resource Speech Recognition Based on Transfer Learning and Language Model Fusion [D]. Northwest University for Nationalities, 2023.
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Liu Ben Research on Natural Language Understanding in AORBCO Models [D]. Xi'an University of Technology, 2023.
[9]
Liu Peng. Key Technology Research on Domain Oriented Knowledge Graph Construction [D]. Xi'an University of Technology, 2023.
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Yang Shuaisong. Research on the construction method of knowledge graph for primary equipment faults in substations [D]. Northeast Electric Power University, 2023.

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  1. A Method for Constructing a Knowledge Graph of Electric Power Digital Marketing Based on Artificial Intelligence Deep Learning

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 31 July 2024

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    Author Tags

    1. Electricity Marketing
    2. Knowledge graph
    3. Natural language processing

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