A Method of Combining Multi-Aspect Information for Qualitative Spatial Reasoning

Qualitative spatial reasoning has been an important context in the area of artificial intelligence. Spatial information includes topology, size, shape, distance, etc. Single-aspect spatial information has been studied for many years. But how to combine the single-aspect information in a frame for representation and reasoning is an important problem. In this paper, we propose a new method for combining multi-aspect information using an operation symbol which is called “combine”. By “combine” operator, one can represent new relations using the single-aspect relation set which is joint exclusive and pair-wise disjoint, and get the rough composition table very easily. Then we give two models. The first one combines the topology and size information and the second one combines the topology and far-near information. We propose a new concept called “neighborhood partition graph”, which could present the relationship among the atom relation in relation set which is joint exclusive and pair-wise disjoint. One can convert the neighborhood partition graph of a new model which combines multi-aspects information into its concept neighborhood graph very easily. We solve the problem proposed by Galton in 1994:“why the case of the line-of-sight relations differs interestingly from the standard spatial and temporal relations in that the result of composing two relations does not always form a conceptual neighborhood graph”.