Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
A spatial configuration of a rudimentary, static, realworld
scene with known objects (animals) and properties
(positions and orientations) contains a wealth of syntactic
and semantic spatial information that can contribute to a
computational understanding far beyond what its
quantitative details alone convey. This work presents an
approach that (1) quantitatively represents what a
configuration explicitly states, (2) integrates this
information with implicit, commonsense background
knowledge of its objects and properties, (3) infers
additional, contextually appropriate, commonsense
spatial information from and about their
interrelationships, and (4) augments the original
representation with this combined information. A
semantic network represents explicit, quantitative
information in a configuration. An inheritance-based
knowledge base of relevant concepts supplies implicit,
qualitative background knowledge to support semantic
interpretation. Together, these structures provide a
simple, nondeductive, constraint-based, geometric logical
formalism to infer substantial implicit knowledge for
intrinsic and deictic frames of spatial reference.
