将地理特征融入知识图谱嵌入，提升空间推理准确性。
Geometric Feature Enhanced Knowledge Graph Embedding and Spatial Reasoning
- 引入拓扑、方向、距离等几何特征增强知识图谱表示
- 在链接预测任务中，拓扑与方向特征显著提升准确率
- 适合从事地理人工智能与空间智能研究的学者
地理知识图谱（GeoKG）以互联方式建模地理实体（如地点和自然特征）及其空间关系，为地理数据检索、问答系统和空间推理提供有力支持。然而，现有知识图谱嵌入（KGE）方法缺乏地理感知能力。本文通过引入空间关系的几何特征——拓扑、方向与距离，提升通用型KGE模型的地理直觉性。新模型在下游链接预测任务中验证，结果显示，尤其拓扑与方向特征的引入显著提升了地理实体与空间关系的预测精度。该研究为将空间概念与原理融入GeoKG挖掘过程提供了新视角，助力定制化地理人工智能解决方案应对复杂地理挑战。
原文摘要 · Abstract (English)
Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support for geographic applications, including data retrieval, question-answering, and spatial reasoning. However, existing methods for mining and reasoning from GeoKGs, such as popular knowledge graph embedding (KGE) techniques, lack geographic awareness. This study aims to enhance general-purpose KGE by developing new strategies and integrating geometric features of spatial relations, including topology, direction, and distance, to infuse the embedding process with geographic intuition. The new model is tested on downstream link prediction tasks, and the results show that the inclusion of geometric features, particularly topology and direction, improves prediction accuracy for both geoentities and spatial relations. Our research offers new perspectives for integrating spatial concepts and principles into the GeoKG mining process, providing customized GeoAI solutions for geospatial challenges.
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