北京大学学报（自然科学版）2026，Vol.62Issue(4)：699-709,11.DOI:10.13209/j.0479-8023.2025.087
融合多源时空知识图谱与图神经网络的智慧选址模型
A Smart Site Selection Model Integrating Multisource Spatiotemporal Knowledge Graphs and Graph Neural Networks
摘要
Abstract
To address the issues of high labor and time costs,neglect of geographic spatial interactions,and coarse handling of high-dimensional unstructured features in existing retail chain store site selection methods,this paper proposes a smart site selection model(SmartSite)that integrates multi-source spatiotemporal knowledge graphs and graph neural networks(GNNs).Specifically,a multi-source spatiotemporal database(MSSTDB)is first established to collect and standardize multi-source spatiotemporal data.Subsequently,a multi-source spatiotemporal knowledge graph(MSSTKG)is constructed to accurately extract spatiotemporal entities and their relationships,and a graph convolutional network(GCN)is employed to derive deep feature representations from geographic data.Finally,an attention mechanism is introduced to dynamically assign feature weights,thereby building an intelligent site selection model.The performance of the proposed model is validated through comparative experiments on benchmark datasets and ablation studies,and the model is further applied to a leading new retail chain enterprise's store location scenario.Experimental results show that,compared with traditional manual approaches and conventional data-driven methods,the proposed model can effectively exploit the correlations inherent in geographic spatial data,significantly reduce site selection costs,and deliver substantial economic benefits,offering a reliable decision-making reference for retail chain enterprises in store location planning.关键词
门店选址/知识图谱(KG)/图神经网络(GNN)/多源时空数据Key words
store site selection/knowledge graphs(KG)/graph neural networks(GNN)/multisource spatiotemporal data引用本文复制引用
毛亮坚,李直旭..融合多源时空知识图谱与图神经网络的智慧选址模型[J].北京大学学报（自然科学版）,2026,62(4):699-709,11.基金项目
苏州市人工智能与社会治理技术重点实验室项目(SZS2023007)、苏州独墅湖科教创新区智能社会治理技术与创新应用平台项目(YZCXPT2023101)以及苏州工业园区领军人才计划(科教领军)资助 （SZS2023007）
