Torsten Hahmann is an assistant professor of spatial informatics at the School of Computing and Information Science and the National Center for Geographic Information and Analysis (NCGIA) at the University of Maine in Orono, ME, USA.
Previously, he worked as a postdoctoral researcher with Sheila McIlraith (University of Toronto) and Autodesk Research on various aspects of ontology-based knowledge representation within the Parametric Human Project. Torsten's expertise lies at the intersection of formal ontology and heterogeneous representations of space. Torsten earned a Ph.D. from the University of Toronto (2013) within the Semantic Technology Lab, under MichaelGruninger's supervision. His dissertation "A Reconciliation of Logical Theories of Space: from Multidimensional Mereotopology to Geometry" is about novel spatial ontologies that bridge qualitative and geometric conceptualizations of physical space, their formal semantic integration and their semi-automated verification. The work contains 116 different spatial ontologies, all checked for consistency and with several hundred properties verified.
Prior to that, Torsten obtained a M.Sc. in Computer Science (2008) from the University of Toronto working on mathematical characterizations of mereotopology, and a B.Sc. in Software Systems Engineering from the Hasso-Plattner-Institute (HPI) Potsdam, Germany.
I am generally interested in everything that involves formal representations of knowledge, so-called knowledge representation. More specifically I'm interested in ontologies, semantic integration, semantic interoperability, and semantic technologies in general. My research interests encompass the following areas:
- Expressive and lightweight ontologies
- Ontology verification, modularity, and repositories and tools to help with ontology design & maintenance & integration
- Semantic technologies, interoperability, data and knowledge integration
- Spatial intelligence, including qualitative spatial reasoning (QSR) and the combination of high-level spatial reasoning with low-level, geometric reasoning
- Spatial ontologies and spatial data, including geospatial data, earth science (geological, hydrological, environmental) data, urban planning data, transportation data, building information, product specifications
- Commonsense representations of space, cognitive and philosophical aspects of space
- Automated reasoning with first-order logic (FOL), FOL theorem proving and model finding
- Geometry, topology, mereology, manifolds
- Mathematical logic, model theory