【摘要】 【目的】地理知识图谱(GeoKG)通过知识图谱的形式化技术，将地理知识表示为计算机可解释、可复用、可推理的知识网络。但由于地理信息分布的稀疏性以及更新的落后性，地理知识图谱往往是不完整的，制约着其应用广度和深度，需要地理知识图谱补全方法来解决其不完整的问题。然而，现有补全方法未充分考虑到地理知识图谱中的语义信息以及地理实体间的交互遵循距离衰减效应，致使嵌入空间难以充分还原地理实体和关系的真实分布，从而限制了补全性能的提升。【方法】本文提出了一种顾及距离衰减效应的地理知识图谱补全方法DDGKGC(Distance-Decaying Effect-Aware Geographic Knowledge Graph Completion method)。该方法首先通过语义信息聚合模块和距离衰减效应感知模块，捕获实体和关系间的语义信息和距离信息；然后，通过基于双注意力机制的表示学习模块自适应地学习实体和关系的邻域信息，得到实体和关系的嵌入表示，最后通过ConvE得分函数进行评分预测，并使用预测结果来完成地理知识图谱补全任务。【结果】为全面评估模型性能，本文在自构建数据集Multi-Geo、CityDirection、CountyDistance及公开数据集Countries-S3上进行了对比实验、消融实验和多维度分析验证。实验结果表明，DDGKGC在MRR、 Hits@1、 Hits@3、 Hits@10等多项指标上表现出色，尤其在全面反映模型性能的MRR指标上相较于对比方法在4个数据集上分别提升4%、3.1%、1.8%和5.2%。此外，通过多维度分析验证评估，证明了DDGKGC能够更合理地建模地理实体关系间的空间和语义关联，从而提升补全结果的准确性与地理合理性。【结论】本文提出的顾及距离衰减效应的地理知识图谱补全方法，不仅有效提升了地理知识图谱补全任务的性能，还展现出良好的泛化能力与应用潜力，同时也为地理知识图谱的深化应用提供了可靠支撑。更多还原

【Abstract】 [Objectives] Geographic Knowledge Graph(GeoKG) employs knowledge graph techniques to represent geographic knowledge as a computer-interpretable, reusable, and inferable knowledge network. However, due to the sparsity of geographic information distribution and outdated updates, GeoKGs are often incomplete, which restricts their breadth and depth of application. Geographic knowledge graph completion techniques are needed to address this incompleteness. Nevertheless, existing knowledge graph completion methods fail to fully account for the semantic information within GeoKGs and the distance-decaying effect governing interactions among geographic entities, resulting in an embedding space that inadequately captures the true distribution of geographic entities and relations, thereby limiting completion performance. [Methods] To address this issue, this study proposes a Distance-Decaying Effect-Aware Geographic Knowledge Graph Completion method(DDGKGC). The method first captures semantic information and distance-related features between entities and relations through a semantic information aggregation module and a distance-decaying effect-aware module. Then, a dual-attention mechanism-based representation learning module adaptively learns neighborhood information of entities and relations to derive their embeddings. Finally, the ConvE scoring function is used for prediction, and the results are applied to complete the GeoKGs. [Results] To comprehensively evaluate model performance,this study conducts comparative experiments, ablation studies, and multi-dimensional validation analyses on the self-constructed datasets Multi-Geo, CityDirection, and CountyDistance, as well as the public dataset Countries-S3. Experimental results demonstrate that DDGKGC achieves outstanding performance across multiple metrics including MRR, Hits@1, Hits@3, and Hits@10. Particularly in terms of MRR, which comprehensively reflects model performance, DDGKGC outperforms the baseline methods by 4%, 3.1%, 1.8%, and 5.2% on the four datasets, respectively. Moreover, through multi-dimensional validation and analysis, it is proven that DDGKGC can more effectively model the spatial and semantic relationships among geographic entities, thereby enhancing the accuracy and geographic plausibility of completion results. [Conclusions] The results demon-strate that the proposed method not only effectively enhances the performance of the geographic knowledge graph completion task but also exhibits strong generalization capability and application potential. Furthermore, it provides reliable support for the advanced application of GeoKGs.更多还原