Abstract
Inference mechanisms about spatial relations constitute an important aspect of spatial reasoning as they allow users to derive unknown spatial information from a set of known spatial relations. When formalized in the form of algebras, spatial-relation inferences represent a mathematically sound definition of the behavior of spatial relations, which can be used to specify constraints in spatial query languages. Current spatial query languages utilize spatial concepts that are derived primarily from geometric principles, which do not necessarily match with the concepts people use when they reason and communicate about spatial relations. This paper presents an alternative approach to spatial reasoning by starting with a small set of spatial operators that are derived from concepts closely related to human cognition. This cognitive foundation comes from the behavior of image schemata, which are cognitive structures for organizing people's experiences and comprehension. From the operations and spatial relations of a small-scale space, a container–surface algebra is defined with nine basic spatial operators—inside, outside, on, off, their respective converse relations—contains, excludes, supports, separated_from, and the identity relation equal. The container–surface algebra was applied to spaces with objects of different sizes and its inferences were assessed through human-subject experiments. Discrepancies between the container–surface algebra and the human-subject testing appear for combinations of spatial relations that result in more than one possible inference depending on the relative size of objects. For configurations with small- and large-scale objects larger discrepancies were found because people use relations such as part of and at in lieu of in. Basic concepts such as containers and surfaces seem to be a promising approach to define and derive inferences among spatial relations that are close to human reasoning.
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