Semantic-aware graph convolution network on multi-hop paths for link prediction  

作　　者：彭斐 CHEN Shudong QI Donglin YU Yong TONG Da PENG Fei(Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029,P.R.China)

机构地区：[1]Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029,P.R.China [2]University of Chinese Academy of Sciences,Beijing 101408,P.R.China

出　　处：《High Technology Letters》2023年第3期269-278,共10页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61876144).

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-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 thresholdKis experimented on the FB15K-237 and WN18RR datasets,and the final results prove the effectiveness of the model.

关 键 词：knowledge graph(KG) link prediction graph convolution network(GCN) knowledge graph completion(KGC) multi-hop paths semantic information 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]