Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220900205-6.doi: 10.11896/jsjkx.220900205

• Artificial Intelligence • Previous Articles     Next Articles

Construction of Badminton Knowledge Graph Completion Model Based on Deep Learning

CHEN Yujue1, HU He2, LI Qiang3   

  1. 1 School of Physical Education,Hunan Normal University,Changsha 410081,China
    2 School of Computer Science,Xi'an University of Science and Technology,Xi'an 710000,China
    3 School of Physical Education,Qinghai Normal University,Xining 810009,China
  • Published:2023-11-09
  • About author:CHEN Yujue,born in 1998,Ph.D.Her main research interest is sports and artificial intelligence.
    HU He,born in 1999,postgraduate.His main research interest is natural language processing.
  • Supported by:
    National Natural Science Foundation of China(11551003).

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Abstract: To enhance the application value of knowledge graph in badminton field,this research first analyzes the research status of the completion model,then combines the deep learning technology and attention mechanism to build a knowledge graph completion model based on graph convolution neural network with subgraph structure decoupling,and finally evaluates the improved performance of the model.The results show that the proposed model has achieved good results in all sub datasets,which is equi-valent to the best baseline model.On the three data sets selected in the experiment,the two test indicators are reduced to varying degrees,which indicates the effectiveness of entity feature decoupling.Only 3 or 8 bases are sufficient to express the characteristics of different relationships in the model.In this paper,a knowledge graph completion model with good improvement effect is obtained.This study lays a foundation for the popularization of knowledge atlas in badminton.

Key words: Knowledge graph completion, Deep learning, Badminton, Graph convolution neural network, Attention mechanism

CLC Number: 

  • TP181
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