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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). \r\nUsing 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. 

During a natural disaster such as flooding, the failure of a single asset in the complex and interconnected web of critical urban infrastructure can trigger a cascade of failures within and across multiple systems with potentially life-threatening consequences. To help emergency management effectively and efficiently assess such failures, we design the Utility Connection Ontology Design Pattern to represent utility services and model connections within and across those services. The pattern is encoded as an OWL ontology and instantiated with utility data in a geospatial knowledge graph. We demonstrate how it facilitates reasoning to identify cascading service failures due to flooding for producing maps and other summaries for situational awareness.