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Contamination by heavy metals, per- and polyfluoroalkyl substances (PFAS), and other emerging pollutants poses serious risks to environmental and human health. Effective monitoring and tracing require integrating data from diverse sources. A knowledge graph approach enables semantic integration, but relies on an ontology that supports intuitive and robust querying and reasoning. To address this, we present the Contaminant Observations and Samples Ontology (ContaminOSO), a framework for semantically enriching environmental contaminant data. Built on SOSA and QUDT ontologies, ContaminOSO introduces key extensions to meet contamination-specific needs and real-world data challenges. This paper highlights four of its core design solutions: (1) extending SOSA to model multiple features of interest; (2) using QUDT to standardize the representation of contaminants and observed properties; (3) developing a detailed and nuanced pattern for measurement result representation using QUDT and STAD; and (4) adopting a pragmatic approach for connecting to existing taxonomies from the OBO Foundry, such as the NCBI organismal classification and relevant subsets of the Food Ontology (FoodOn), for classifying samples. 

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. 

While OWL and RDF are by far the most popular logic-based languages for Semantic Web Ontologies, some well-designed ontologies are only available in languages with a much richer expressivity, such as first-order logic (FOL) or the ISO standard Common Logic. This inhibits reuse of these ontologies by the wider Semantic Web Community. While converting OWL ontologies to FOL is straightforward, the reverse problem of finding the closest OWL approximation of an FOL ontology is undecidable. However, for most practical purposes, a ``good enough'' OWL approximation need not be perfect to enable wider reuse by the Semantic Web Community. This paper outlines such a conversion approach by first normalizing FOL sentences into a function-free prenex conjunctive normal (FF-PCNF) that strips away minor syntactic differences and then applying a pattern-based approach to identify common OWL axioms. It is tested on the over 2,000 FOL ontologies from the Common Logic Ontology Repository.