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1. Precomputed Topological Relations for Integrated Geospatial Analysis Across Knowledge Graphs

   https://doi.org/10.4230/lipics.giscience.2025.4  

   Schweikert, Katrina; Kedrowski, David K; Stephen, Shirly; Hahmann, Torsten (January 2025, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Sila-Nowicka, Katarzyna; Moore, Antoni; O'Sullivan, David; Adams, Benjamin; Gahegan, Mark (Ed.)

   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. 

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2. Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach

   https://doi.org/10.32473/flairs.36.133253  

   Abbas, Asim; Khalid, Mutahira; Chalarca, Sebastian; Keshtkar, Fazel; Bukhari, Syed Ahmad (May 2023, The International FLAIRS Conference Proceedings)

   An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically 

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3. Enhancing Biomedical Semantic Annotations through a Knowledge Graph-Based Approach

   https://doi.org/10.32473/flairs.36.133253  

   Abbas, A.; Khalid, M; Chalarca, S; Keshtkar, F; Bukhari, S. A. (August 2023, VOL. 36 (2023): PROCEEDINGS OF FLAIRS-36)

   An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically 

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4. Enhanced Deep Learning Super-Resolution for Bathymetry Data

   https://doi.org/10.1109/BDCAT56447.2022.00014  

   Li, Xingyan; Li, Jian; Williams, Zachary; Huang, Xin; Carroll, Mark; Wang, Jianwu (January 2023, 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies (BDCAT 2022))

   Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained super-resolution deep learning model, namely SR-ResNet, for high-resolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM. 

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5. ContaminOSO: Ontological Foundations and Design Choices for an Ontology for Environmental Contamination Data

   https://doi.org/10.3233/FAIA250501  

   Hahmann, Torsten; Schweikert, Katrina; Stephen, Shirly; Kedrowski, David (August 2025, IOS Press)

   Prince_Sales, Tiago; Masolo, Claudio; Keet, Maria (Ed.)

   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. 

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