Torsten Hahmann

Office: 344 Boardman Hall

Lab: 236A Boardman Hall

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Dr. Hahmann is an Associate Professor in the School of Computing and Information Science and is affiliated with the Spatial Data Science Institute at the University of Maine. He joined the department in 2013. He directs the Spatial Knowledge and Artificial Intelligence (SKAI) lab. Previously, he worked as a postdoctoral researcher with Autodesk Research.


  • Ph.D. University of Toronto (2013)
  • M.Sc. University of Toronto (2008)

Research Interests

Dr. Hahmann’s research covers the areas of spatial informatics and artificial intelligence, specifically, knowledge representation, logic, and automated reasoning.
His current research resolves around how rich semantic descriptions of the information from complex and heterogeneous information systems in formal logical representations – so-called ontologies – can be efficiently obtained and integrated with one another. Of particular interest are spatial ontologies, which encode the information contained in geographic, geological, hydrological, or environmental maps (as typically stored in geographic information systems), sketch maps, verbal route descriptions and indoor environments (think CAD and Building Information Models describing buildings, rooms, etc.), or biological systems (such as the human body or a single bone).

Dr. Hahmann’s work develops and studies spatial ontologies for data integration and sharing.
One aspect focuses on how to move between precise computational spatial information (e.g. from geographic information systems, building information systems, or computer-aided design software) and intuitive, but often more vague descriptions of space (using vague, more qualitative spatial terms such as North, South, in, next to, between) commonly used by people.
Another focuses on semantically integrating and connecting geospatial data from different sources to make it more accessible and to uncover new knowledge from it
His work is highly interdisciplinary, touching upon computer science, geography, philosophy, information science, and cognitive science, and ranges from very theoretical work that advance logic-based AI in the broadest sense to more domain specific work on modeling knowledge about hydrology, geology, glaciology, or environmental sciences.

Current research projects include a $6M project on building an Urban Flooding Open Knowledge Network (UF-OKN), a $1.5M project “Safe Agriculture and Water Graph (SAWGraph)” integrating data on PFAS contamination both funded by NSF, and a USDA funded project on a knowledge graph (CelloGraph) for Cellulose Material Informatics. Another long-term project in collaboration with the Geological Survey of Canada focuses on formally capturing the semantics of different kinds of water features (e.g., lakes, river systems, aquifers, wells, etc.). He also leads the development of the macleod toolkit for automated reasoning on ontologies specified in Common Logic.