Mental models: towards a cognitive science of language, inference, and consciousness
Mental models: towards a cognitive science of language, inference, and consciousness
Qualitative representation of positional information
Artificial Intelligence
Qualitative Representation of Spatial Knowledge
Qualitative Representation of Spatial Knowledge
Reference Frames for Spatial Inference in Text Understanding
Spatial Cognition, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge
Spatial Inference - Learning vs. Constraint Solving
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
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We present an approach to spatial inference which is based on the procedural semantics of spatial relations. In contrast to qualitative reasoning, we do not use discrete symbolic models. Instead, relations between pairs of objects are represented by parameterized homogeneous transformation matrices with numerical constraints. A textual description of a spatial scene is transformed into a graph with objects and annotated local reference systems as nodes and relations as arcs. Inference is realized by multiplication of transformation matrices, constraint propagation and verification. Constraints consisting of equations and inequations containing trigonometric functions can be solved using machine learning techniques. By assigning values to the parameters and using heuristics for the placement of objects, a visualization of the described spatial layout can be generated from the graph.