重庆邮电大学学报（自然科学版）2025，Vol.37Issue(5)：708-716,9.DOI:10.3979/j.issn.1673-825X.202408040201
融合跨时间共性特征的时序知识图谱推理模型
Temporal knowledge graph reasoning model incorporating cross-time commonality features
摘要
Abstract
Temporal knowledge graph reasoning,which predicts events absent from the graph,has seen significant applica-tions in recommendation systems,question answering,and healthcare.The lack of background knowledge in temporal knowledge graphs hinders reasoning,with existing methods relying on external graphs while overlooking implicit data within the graph.To fully exploit the graph's implicit background information,this paper extracts cross-temporal features to define entity backgrounds and proposes a temporal knowledge graph reasoning model incorporating cross-time commonality features(TR-CTC).TR-CTC uses a graph neural network to extract cross-temporal commonality from multi-hop paths,integrating it as background information into the graph representation learning process,enhancing reasoning performance.Experimental results show that TR-CTC generally outperforms baseline models in link prediction tasks.关键词
时序知识图谱/图神经网络/链接预测/跨时间共性/知识图谱推理Key words
temporal knowledge graph/graph neural network/link prediction/cross-temporal commonality/knowledge graph reasoning分类
电子信息工程引用本文复制引用
陈美琪,张诚麟,于洪..融合跨时间共性特征的时序知识图谱推理模型[J].重庆邮电大学学报（自然科学版）,2025,37(5):708-716,9.基金项目
国家自然科学基金项目(62136002、62221005) （62136002、62221005）
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0578)National Natural Science Foundation of China(62136002,62221005) （CSTB2022NSCQ-MSX0578）
General Project of Chongqing Natural Sci-ence Foundation(CSTB2022NSCQ-MSX0578) （CSTB2022NSCQ-MSX0578）
