Construction of transformer substation fault knowledge graph based on a depth learning algorithm

被引:5
作者
Zhu, Deliang [1 ]
Zeng, Weihua [1 ]
Su, Jianming [1 ]
机构
[1] State Grid Anhui Elect Power Co, Tongling Power Supply Co, Hefei, Anhui, Peoples R China
关键词
Knowledge graph; AI; substation failure; structure; fault knowledge; depth learning algorithm;
D O I
10.1142/S1793962323410179
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
A knowledge graph is a visual method that can display the information contained in the knowledge points, core structure, and comprehensive knowledge structure technology. In recent years, with the innovation of science and technology, the business field became keen on knowledge graphs and the graphical display method. However, the application of knowledge graphs in the business field is mainly limited to search engines, question, and answer systems because of the technical difficulties of knowledge extraction and knowledge graph drawing of unstructured text, especially the knowledge extraction of amorphous culture. It can provide knowledgeable service to users by analyzing the knowledge entity contained in encyclopedia knowledge or knowledge base. This paper will focus on the critical link of knowledge extraction of the knowledge graph, adopt a depth learning algorithm to solve this urgent problem and combine with the application of knowledge graph in substation fault to analyze the construction process of substation fault knowledge map based on AI.
引用
收藏
页数:15
相关论文
共 17 条
  • [1] Learning Deep Architectures for AI
    Bengio, Yoshua
    [J]. FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2009, 2 (01): : 1 - 127
  • [2] Linked Data - The Story So Far
    Bizer, Christian
    Heath, Tom
    Berners-Lee, Tim
    [J]. INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS, 2009, 5 (03) : 1 - 22
  • [3] Assessment of learning in environments interactive through fuzzy cognitive maps
    Bolivar Baron, Holman
    Gonzalez Crespo, Ruben
    Pascual Espada, Jordan
    Sanjuan Martinez, Oscar
    [J]. SOFT COMPUTING, 2015, 19 (04) : 1037 - 1050
  • [4] Bollacker K. D., 2007, PROC 22 AAAI C ARTIF, P1962
  • [5] Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion
    Dong, Xin Luna
    Gabrilovich, Evgeniy
    Heitz, Geremy
    Horn, Wilko
    Lao, Ni
    Murphy, Kevin
    Strohmann, Thomas
    Sun, Shaohua
    Zhang, Wei
    [J]. PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), 2014, : 601 - 610
  • [6] Jiang X., 2013, LIBR INF, P23, DOI [10.3969/j.issn.1003-6938.2013.06.005, DOI 10.3969/J.ISSN.1003-6938.2013.06.005]
  • [7] Li Y., 2019, E-Educ. Res., V8, P60
  • [8] [刘峤 Liu Qiao], 2016, [计算机研究与发展, Journal of Computer Research and Development], V53, P582
  • [9] Ma, 2021, J PHYS C SERIES, V1871
  • [10] Processing knowledge graph-based complex questions through question decomposition and recomposition
    Shin, Sangjin
    Lee, Kyong-Ho
    [J]. INFORMATION SCIENCES, 2020, 523 : 234 - 244