摘要
为提升知识图谱在羽毛球领域的应用价值,首先对补全模型的研究现状进行了分析,其次结合深度学习技术和注意力机制,基于图卷积神经网络构建子图结构解耦的知识图谱补全模型,最后对模型的改进性能进行评估。结果表明,所提模型在所有子数据集都取得了良好的结果,与最佳基线模型相当;在实验中选择的3个数据集上,两个测试指标都有不同程度的降低,这表明了实体特征解耦的有效性;只需3个或8个基底就足以表达模型中不同关系的特征。本研究得到了改进效果良好的知识图谱补全模型,为知识图谱在羽毛球领域的推广奠定了基础。
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.
作者
陈玉珏
胡赫
李强
CHEN Yujue;HU He;LI Qiang(School of Physical Education,Hunan Normal University,Changsha 410081,China;School of Computer Science,Xi’an University of Science and Technology,Xi’an 710000,China;School of Physical Education,Qinghai Normal University,Xining 810009,China)
出处
《计算机科学》
CSCD
北大核心
2023年第S02期115-120,共6页
Computer Science
基金
国家自然科学基金(11551003)。
关键词
知识图谱补全
深度学习
羽毛球
图卷积神经网络
注意力机制
Knowledge graph completion
Deep learning
Badminton
Graph convolution neural network
Attention mechanism
