Abstract
The Spatial Knowledge Graphs (SKG) are experiencing growing adoption as a means to model real-world entities, proving especially invaluable in domains like crisis management and urban planning. Considering that RDF specifications offer limited support for effectively managing spatial information, it's common practice to include text-based serializations of geometrical features, such as polygons and lines, as string literals in knowledge graphs. Consequently, Spatial Knowledge Graphs (SKGs) often rely on geo-enabled RDF Stores capable of parsing, interpreting, and indexing such serializations. In this paper, we leverage grid cells as the foundational element of SKGs and demonstrate how efficiently the spatial characteristics of real-world entities and their attributes can be encoded within knowledge graphs. Furthermore, we introduce a novel methodology for representing street networks in knowledge graphs, diverging from the conventional practice of individually capturing each street segment. Instead, our approach is based on tessellating the street network using grid cells and creating a simplified representation that could be utilized for various routing and navigation tasks, solely relying on RDF specifications.
Abstract (translated)
空间知识图谱（SKG）正日益被采用作为建模现实世界实体的一种手段，尤其是在危机管理和城市规划等领域证明了其不可替代的价值。考虑到RDF规范在有效管理空间信息方面提供的支持有限，将几何特征（如多边形和线）的文本序列化形式以字符串字面量的形式包含在知识图谱中是一种常见做法。因此，空间知识图谱（SKGs）通常依赖于具备解析、解释和索引这些序列化的地理增强型RDF存储系统。在这篇论文中，我们利用网格单元作为SKGs的基础元素，并展示了如何高效地将现实世界实体及其属性的空间特征编码到知识图谱中。此外，我们提出了一种表示街道网络的新方法，与传统上分别捕获每个街道段的做法不同，我们的方法基于使用网格单元对街道网络进行密铺（tessellation），创建一种简化表示形式，仅依靠RDF规范即可用于各种路线规划和导航任务。
URL
https://arxiv.org/abs/2411.02309
