# The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs

arXiv: https://arxiv.org/abs/2410.14808 · HTML: https://mlsanity.com/paper/2410.14808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 101 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018)
- [Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions](https://mlsanity.com/paper/2105.09392/) (2021)
- [Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags](https://mlsanity.com/paper/2111.00970/) (2021)
- [EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System](https://mlsanity.com/paper/2304.04893/) (2023)
- [GeoSPARQL+: Syntax, Semantics and System for Integrated Querying of Graph, Raster and Vector Data -- Technical Report](https://mlsanity.com/paper/2009.05032/) (2020)
- [A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid](https://mlsanity.com/paper/2405.15375/) (2024)
- [Evaluating Geospatial RDF stores Using the Benchmark Geographica 2](https://mlsanity.com/paper/1906.01933/) (2019)
- [Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey](https://mlsanity.com/paper/2009.10331/) (2020)
- [A GeoSPARQL Compliance Benchmark](https://mlsanity.com/paper/2102.06139/) (2021)

## Built on by (most cited first, 1 of 1)

- [Toward precision soil health: A regional framework for site-specific management across Missouri](https://mlsanity.com/paper/2510.26815/) (2025)

## Abstract (arXiv)

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.