Open AccessKnowledge Graph Completion Based on GCN of Multi‐Information Fusion and High‐Dimensional Structure Analysis Weight
Author(s) -
NIU Haoran,
HE Haitao,
FENG Jianzhou,
NIE Junlan,
ZHANG Yangsen,
REN Jiadong
Publication year - 2022
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.00.080
Subject(s) - decodes , graph , computer science , node (physics) , adjacency matrix , theoretical computer science , encode , convolution (computer science) , feature (linguistics) , algorithm , artificial intelligence , decoding methods , artificial neural network , biochemistry , chemistry , linguistics , philosophy , structural engineering , engineering , gene
Knowledge graph completion (KGC) can solve the problem of data sparsity in the knowledge graph. A large number of models for the KGC task have been proposed in recent years. However, the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model, which leads to relatively single encoding information. To this end, a new KGC model that encodes and decodes the feature information is proposed. First, we adopt the subgraph sampling method to extract node structure. Moreover, the graph convolutional network (GCN) introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information. Eventually, the high‐dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function. The experimental results show that the model performs well on the datasets used.