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Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs

Authors Katrina Schweikert , David K. Kedrowski , Shirly Stephen , Torsten Hahmann

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LIPIcs.GIScience.2025.4.pdf
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ACM Subject Classification
  • Computing methodologies → Spatial and physical reasoning
  • Computing methodologies → Ontology engineering
Keywords
  • knowledge graph
  • GeoKG
  • spatial analysis
  • ontology
  • SPARQL
  • GeoSPARQL
  • discrete global grid system
  • S2 geometry
  • GeoAI
  • PFAS

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Abstract

Geospatial Knowledge Graphs (GeoKGs) represent a significant advancement in the integration of AI-driven geographic information, facilitating interoperable and semantically rich geospatial analytics across various domains. This paper explores the use of topologically enriched GeoKGs, built on an explicit representation of S2 Geometry alongside precomputed topological relations, for constructing efficient geospatial analysis workflows within and across knowledge graphs (KGs). 
Using the SAWGraph knowledge graph as a case study focused on enviromental contamination by PFAS, we demonstrate how this framework supports fundamental GIS operations - such as spatial filtering, proximity analysis, overlay operations and network analysis - in a GeoKG setting while allowing for the easy linking of these operations with one another and with semantic filters. This enables the efficient execution of complex geospatial analyses as semantically-explicit queries and enhances the usability of geospatial data across graphs. Additionally, the framework eliminates the need for explicit support for GeoSPARQL’s topological operations in the utilized graph databases and better integrates spatial knowledge into the overall semantic inference process supported by RDFS and OWL ontologies.

Cite As Get BibTex

Katrina Schweikert, David K. Kedrowski, Shirly Stephen, and Torsten Hahmann. Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.GIScience.2025.4

Author Details

Katrina Schweikert
  • School of Computing and Information Science, University of Maine, Orono, ME, USA
David K. Kedrowski
  • School of Computing and Information Science, University of Maine, Orono, ME, USA
Shirly Stephen
  • NCEAS, Department of Geography, University of California, Santa Barbara, CA, USA
  • School of Computing and Information Science, University of Maine, Orono, ME, USA
Torsten Hahmann
  • School of Computing and Information Science, University of Maine, Orono, ME, USA

Funding

This research has been supported by the National Science Foundation under Grant No. 2333782: "Safe Agricultural Products and Water Graph (SAWGraph): An OKN to Monitor and Trace PFAS and Other Contaminants in the Nation’s Food and Water Systems", which is part of a larger NSF effort to develop a Prototype Open Knowledge Network (Proto-OKN: https://www.proto-okn.net/). Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Acknowledgements

We are grateful to the entire SAWGraph team and all collaborators from federal and state agencies, especially Maine DEP and DACF and Antony Williams and Vasu Kilaru from EPA for their invaluable collaboration on the SAWGraph project. Furthermore, we thank the other Proto-OKN teams for their input on the spatial knowledge graph and the five anonymous reviewers for their constructive comments that helped clarify the presentation.

Supplementary Materials

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