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Research on Association Relation Reasoning Algorithm for Spacecraft Fault Knowledge Graph
knowledge graph, relation reasoning, knowledge representation
Final Paper
Yizhou Ding / Beihang university
Diyin Tang / Beihang University (Beijing University of Aeronautics and Astronautics)
Yuanjin Laili / Beihang University (Beijing University of Aeronautics and Astronautics)
Jinsong Yu / Beihang University (Beijing University of Aeronautics and Astronautics)
In this paper, based on the knowledge graph of spacecraft fault, the methods of semantic representation learning and graph embedding, which are commonly used in the field of knowledge graph embedding, are explored. To address the problem of insufficient information in the adjacent subgraphs, a GraphSAGE graph neural network method is proposed for secondary aggregation to construct node embedding and feature extraction. These methods accomplish the transformation from graphs to computer-recognizable vectors, and furthermore, relational reasoning with multi-feature fusion is realized using the attention mechanism. In addition, for the problem of different training mechanisms with different embedding modes, the embedding effect is evaluated using labeled multiclassification assessment to target the embedding quality and improve the reasoning effect. Finally, the reasoning methods used in this paper are comparatively analyzed by means of experiments.
Important Date
  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University