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Construction of Hierarchical Knowledge Graph Based on Electromechanical Equipment

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Published:17 March 2022Publication History

ABSTRACT

The purpose of constructing the knowledge graph of electromechanical equipment is to make full use of the data and information carried by electromechanical equipment, constructing the knowledge association between each independent equipment. The knowledge graph of electromechanical equipment uses a structured way to express the relationship between concepts and entities in electromechanical equipment, and uses this form for data storage and computational reasoning. This can simplify the design of the electromechanical system and improve the efficiency of knowledge reasoning and knowledge calculation. However, due to the complex structure, high correlation and large amount of information of electromechanical equipment, the constructed knowledge graph of electromechanical equipment often has the problem of insufficient association expression and sparse information. Especially lack of expression of its hierarchical relationship. Therefore, this paper proposes a hierarchical relationship construction method based on electromechanical equipment, which is specifically used to extract the information about the physical model, logical model and geometric model of power distribution equipment, electrical equipment, cables, and pipelines in electromechanical equipment, constructing a Hierarchical knowledge graph. The hierarchical relationship construction method is mainly through the integration of logical and physical levels, hierarchical structure of electromechanical equipment data and data clustering as an important feature, so as to obtain stable and hierarchical characteristics of electromechanical equipment knowledge graph. At the same time, the hierarchical features can further complete the construction of the knowledge graph, thereby improving the structure of the knowledge graph and efficiency of the reasoning engine.

References

  1. Kejriwal Mayank, Sequeda Juan, Lopez Vanessa  Knowledge graphs: Construction, management and querying[J] Semantic Web, 2019, 10(6)Google ScholarGoogle Scholar
  2. Wang Zikang, Li Linjing, Zeng Daniel SRGCN: Graph-based multi-hop reasoning on knowledge graphs[J] Neurocomputing, 2021, 454Google ScholarGoogle Scholar
  3. Wang Meihong, Qiu Linling, Wang Xiaoli A Survey on Knowledge Graph Embeddings for Link Prediction[J] Symmetry, 2021, 13(3)Google ScholarGoogle Scholar
  4. Kemas Wiharja, Jeff Z. Pan, Martin J. Kollingbaum Schema aware iterative Knowledge Graph completion[J] Journal of Web Semantics, 2020Google ScholarGoogle Scholar
  5. Mutlu Ece C., Oghaz Toktam, Rajabi Amirarsalan  Review on Learning and Extracting Graph Features for Link Prediction[J] Machine Learning and Knowledge Extraction, 2020, 2(4)Google ScholarGoogle Scholar
  6.  Accenture Global Solutions Limited; Patent Application Titled "Knowledge Graph Weighting During Chatbot Sessions" Published Online (USPTO 20200074319) [J] Computer Weekly News, 2020Google ScholarGoogle Scholar
  7. Wang Shuo, Zhong Yi, Wang Chengpeng Attention Relational Graph Convolution Networks for Relation Prediction in Knowledge Graphs[J] Journal of Physics: Conference Series, 2021, 1848(1).Google ScholarGoogle Scholar
  8. Apple Inc.; Patent Issued for Knowledge Graph Metadata Network Based on Notable Moments (USPTO 10,324,973) [J] Computer Weekly News, 2019Google ScholarGoogle Scholar
  9. Lee Wan-Kon, Shin Won-Chul, Jagvaral Batselem  A path-based relation networks model for knowledge graph completion[J] Expert Systems with Applications, 2021, 182Google ScholarGoogle Scholar
  10. Krinkin K.V., Vodyaho A.I., Kulikov I.A.  The method of inductive synthesis of hierarchical knowledge graphs of telecommunication networks based on statistical data[J] Procedia Computer Science, 2021, 186Google ScholarGoogle Scholar
  11.  Temporal Reasoning; Reports Summarize Temporal Reasoning Study Results from University of Huddersfield (Large scale distributed spatio-temporal reasoning using real-world knowledge graphs) [J] Journal of Engineering, 2019Google ScholarGoogle Scholar
  12. Liu Luwei, Zhu Cui, Zhu Wenjun Knowledge Graph Completion Based on Graph Representation and Probability Model[J] Journal of Physics: Conference Series, 2021, 1757(1)Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    ICSCC '21: Proceedings of the 2021 6th International Conference on Systems, Control and Communications
    October 2021
    59 pages
    ISBN:9781450389006
    DOI:10.1145/3510362

    Copyright © 2021 ACM

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    New York, NY, United States

    Publication History

    • Published: 17 March 2022

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