计算机应用研究2026，Vol.43Issue(4)：1013-1020,8.DOI:10.19734/j.issn.1001-3695.2025.08.0296
时空融合的时序知识图谱多跳推理模型
Spatiotemporal fusion multi-hop reasoning model for temporal knowledge graphs
摘要
Abstract
To address the issues of semantic disconnection caused by separated entity-relation embedding spaces and limited temporal expressiveness in existing temporal knowledge graph multi-hop reasoning models,this paper proposed a spatiotempo-ral fusion multi-hop reasoning model(SF-MR).The model incorporated a triple distributor with dual-path residual connections and spatial convolution to capture cross-space semantic dependencies between entities and relations.It introduced a spatiotem-poral attention mechanism to jointly model entity temporal evolution and spatial correlations,dynamically fused via a gated net-work.A hierarchical reinforcement learning framework decoupled reasoning into relation-level and entity-level decisions,alle-viating action space explosion.Experiments on four benchmark datasets(ICEWS14,ICEWS18,WIKI,YAGO)demonstrate that SF-MR outperforms state-of-the-art baselines across multiple metrics.Specifically,on ICEWS14,SF-MR improves MRR,hits@3,and hits@10 by 1.10％,1.53％,and 2.69％,respectively,over the best baseline.Consistent improvements of 0.79％to 1.01％are observed on WIKI and YAGO.Ablation studies confirm the effectiveness of the triple distributor and spatiotemporal attention in enhancing semantic interaction and temporal modeling.关键词
时序知识图谱/多跳推理/三联体分配器/时空注意力/分层强化学习框架Key words
temporal knowledge graph(TKG)/multi-hop reasoning/triplet distributor/spatiotemporal attention/hierarchi-cal reinforcement learning framework分类
信息技术与安全科学引用本文复制引用
马汉达,费凡..时空融合的时序知识图谱多跳推理模型[J].计算机应用研究,2026,43(4):1013-1020,8.基金项目
镇江市重点研发计划资助项目(GY2023034) （GY2023034）
