Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
ERNEST: A Semantic Network System for Pattern Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Man-Machine Studies
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Category theory for computing science, 2nd ed.
Category theory for computing science, 2nd ed.
WordNet: a lexical database for English
Communications of the ACM
Haskell: the craft of functional programming
Haskell: the craft of functional programming
Unifying heterogeneous information models
Communications of the ACM
Semantic Modeling for the Acquisition of Topographic Information from Images and Maps: Smati 97
Semantic Modeling for the Acquisition of Topographic Information from Images and Maps: Smati 97
Shape Nouns and Shape Concepts: A Geometry for 'Corner'
Spatial Cognition, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge
What Are Sports Grounds? Or: Why Semantics Requires Interoperability
INTEROP '99 Proceedings of the Second International Conference on Interoperating Geographic Information Systems
INTEROP '99 Proceedings of the Second International Conference on Interoperating Geographic Information Systems
Asessing Semnatic Similarities among Geospatial Feature Class Definitions
INTEROP '99 Proceedings of the Second International Conference on Interoperating Geographic Information Systems
An interactive visual language for term subsumption languages
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Taxonomies of visual programming and program visualization
Journal of Visual Languages and Computing
Modeling the Semantics of Geographic Categories through Conceptual Integration
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Hi-index | 0.00 |
Semantic networks are among the most popular knowledge reresentation techniques. They have been applied to a large spectrum of applications, including spatial tasks, such as object recognition from images. Their appeal lies in the combination of a structure that combines standard abstraction mechanisms with a simple visual representation. However, applications of semantic networks suffer from their lack of theoretical foundations. The semantics of spatial domains is often modeled with a technique whose semantics are themselves unclear. In our work on semantic interoperability of GIS, we have found this situation to be potentially harmful, but also repairable by our tools. It can be harmful by luring necessary work on application semantics into potentially muddy waters. And it is repairable by interpreting semantic networks from the point of view of algebra, i.e. another semantic modeling technique. Thus, the paper proposes an algebraic interpretation of semantic networks, showing how this perspective clarifies their own semantics and how it allows for a sound semantic modeling of spatial domains.