用地理信息增强知识图谱实体表示，提升位置感知能力。
GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs
- 通过地理节点扩散扩展图结构，融合空间权重进行路径采样。
- 在多个数据集上优于传统RDF2Vec和GeoTransE，提升位置相关任务性能。
- 适合需要地理上下文的推荐、导航与空间推理场景。
许多知识图谱包含大量空间实体，如城市、建筑和自然地标，且部分实体存储了精确几何信息。然而，现有实体表示学习方法大多忽略这些几何数据。本文提出GeoRDF2Vec，一种改进的RDF2Vec模型，通过从地理节点出发进行图遍历扩散，确保所有可达节点被纳入考虑，并基于空间权重对图游走过程施加偏置。在多个基准数据集上的实验表明，该方法在位置感知任务中显著优于非位置感知的RDF2Vec与GeoTransE模型。
原文摘要 · Abstract (English)
Many knowledge graphs contain a substantial number of spatial entities, such as cities, buildings, and natural landmarks. For many of these entities, exact geometries are stored within the knowledge graphs. However, most existing approaches for learning entity representations do not take these geometries into account. In this paper, we introduce a variant of RDF2Vec that incorporates geometric information to learn location-aware embeddings of entities. Our approach expands different nodes by flooding the graph from geographic nodes, ensuring that each reachable node is considered. Based on the resulting flooded graph, we apply a modified version of RDF2Vec that biases graph walks using spatial weights. Through evaluations on multiple benchmark datasets, we demonstrate that our approach outperforms both non-location-aware RDF2Vec and GeoTransE.
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