文章摘要
PENG Fei(彭斐),CHEN Shudong,QI Donglin,YU Yong,TONG Da.[J].高技术通讯(英文),2023,29(3):269~278
Semantic-aware graph convolution network on multi-hop paths for link prediction
  
DOI:10. 3772/ j. issn. 1006-6748. 2023. 03. 005
中文关键词: 
英文关键词: knowledge graph ( KG), link prediction, graph convolution network ( GCN),knowledge graph completion (KGC),multi-hop paths,semantic information
基金项目:
Author NameAffiliation
PENG Fei(彭斐) (Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029,P. R. China) (University of Chinese Academy of Sciences,Beijing 101408,P. R. China) 
CHEN Shudong  
QI Donglin  
YU Yong  
TONG Da  
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中文摘要:
      
英文摘要:
      Knowledge graph (KG) link prediction aims to address the problem of missing multiple valid triples in KGs. Existing approaches either struggle to efficiently model the message passing process of multi-hop paths or lack transparency of model prediction principles. In this paper,a new graph convolutional network path semantic-aware graph convolution network (PSGCN) is proposed to achieve modeling the semantic information of multi-hop paths. PSGCN first uses a random walk strategy to obtain all k-hop paths in KGs,then captures the semantics of the paths by Word2Sec and long shortterm memory (LSTM) models,and finally converts them into a potential representation for the graph convolution network (GCN) messaging process. PSGCN combines path-based inference methods and graph neural networks to achieve better interpretability and scalability. In addition,to ensure the robustness of the model,the value of the path threshold K is experimented on the FB15K-237 and WN18RR datasets,and the final results prove the effectiveness of the model.
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