计算机工程与应用2025，Vol.61Issue(18)：166-174,9.DOI:10.3778/j.issn.1002-8331.2406-0236
基于联合卷积的时序知识图谱推理
Temporal Knowledge Graph Reasoning Based on Joint Convolution
摘要
Abstract
Existing temporal knowledge graph reasoning models fail to fully explore the structural dependencies and potential relationships among concurrent facts in temporal knowledge graphs.Additionally,these models often rely on simplistic and unreasonable time encoding methods,resulting in inadequate temporal information acquisition.This paper proposes a temporal knowledge graph reasoning model based on joint convolution.This model uses a joint aggregator in a graph convolutional neural network to capture the surface semantics and latent features of node neighborhood informa-tion.It also employs vector and event attribute encoding for time to capture rich temporal information,enhancing the temporal sensitivity of the model.Experimental results on ICEWS14,ICEWS05-15,YAGO,and GDELT datasets demon-strate that the model consistently outperforms baseline models in MRR,Hits@1,Hits@3,and Hits@10,as well as in rela-tion prediction.关键词
时序知识图谱/图卷积神经网络/门控循环单元/联合卷积Key words
temporal knowledge graph/graph convolutional network/gated recurrent unit/joint convolution分类
信息技术与安全科学引用本文复制引用
张成珅,马汉达..基于联合卷积的时序知识图谱推理[J].计算机工程与应用,2025,61(18):166-174,9.基金项目
镇江市重点研发计划项目(GY2023034). （GY2023034）
