用S2网格系统统一管理多源地理数据，提升知识图谱的查询与集成效率。
The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs
- 采用S2离散全球网格系统实现地理数据的分层编码与空间对齐
- 支持跨知识图谱的拓扑关系快速查询，减少计算开销
- 适合需要处理大规模多尺度地理数据的研究者与开发者
地理空间知识图谱（GeoKG）已成为地理空间人工智能领域的重要组成部分。美国国家科学基金会的开放知识网络计划旨在构建全国规模、跨学科的GeoKG生态系统，提供符合FAIR原则的AI可用地理数据。然而，该基础设施建设面临三大挑战：1）海量数据管理；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混杂。离散全球网格系统（DGGS）通过高效的表示与整合策略缓解这些问题。KnowWhereGraph利用谷歌的S2几何框架——一种DGGS——实现多源数据高效处理、定性空间查询与跨图谱集成。本文阐述了S2在KnowWhereGraph中的实现，强调其在拓扑增强与语义压缩方面的核心作用。结果表明，DGGS框架，尤其是S2，对构建可扩展的GeoKG具有重要潜力。
原文摘要 · Abstract (English)
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.
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