Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Geospatial Artificial Intelligence. Initiatives like the U.S. National Science Foundation's Open Knowledge Network program aim to create an ecosystem of nation-scale, cross-disciplinary GeoKGs that provide AI-ready geospatial data aligned with FAIR principles. However, building this infrastructure presents key challenges, including 1) managing large volumes of data, 2) the computational complexity of discovering topological relations via SPARQL, and 3) conflating multi-scale raster and vector data. Discrete Global Grid Systems (DGGS) help tackle these issues by offering efficient data integration and representation strategies. The KnowWhereGraph utilizes Google's S2 Geometry -- a DGGS framework -- to enable efficient multi-source data processing, qualitative spatial querying, and cross-graph integration. This paper outlines the implementation of S2 within KnowWhereGraph, emphasizing its role in topologically enriching and semantically compressing data. Ultimately, this work demonstrates the potential of DGGS frameworks, particularly S2, for building scalable GeoKGs.

翻译：地理空间知识图谱已成为地理空间人工智能这一新兴领域不可或缺的组成部分。诸如美国国家科学基金会开放知识网络计划等项目，旨在构建一个国家规模、跨学科的地理空间知识图谱生态系统，以提供符合FAIR原则、可供人工智能直接使用的标准化地理空间数据。然而，构建这一基础设施面临若干关键挑战，包括：1) 海量数据的管理；2) 通过SPARQL发现拓扑关系的计算复杂性；3) 多尺度栅格与矢量数据的融合。离散全球网格系统通过提供高效的数据集成与表示策略，有助于应对这些问题。KnowWhereGraph利用谷歌的S2几何库——一种DGGS框架——实现了高效的多源数据处理、定性空间查询以及跨图谱集成。本文概述了S2在KnowWhereGraph中的实现，重点阐述了其在拓扑关系丰富化与数据语义压缩方面的作用。最终，本研究证明了DGGS框架（特别是S2）在构建可扩展地理空间知识图谱方面的潜力。