Integration of weighted knowledge bases
Artificial Intelligence
Combining topological and size information for spatial reasoning
Artificial Intelligence
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Artificial Intelligence - Special issue on nonmonotonic reasoning
A complete classification of tractability in RCC-5
Journal of Artificial Intelligence Research
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Combining topological and directional information for spatial reasoning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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This paper addresses the problem of merging qualitative constraint networks (QCNs) defined on different qualitative formalisms. Our model is restricted to formalisms where the entities and the relationships between these entities are defined on the same domain. The method is an upstream step to a previous framework dealing with a set of QCNs defined on the same formalism. It consists of translating the input QCNs into a well-chosen common formalism. Two approaches are investigated: in the first one, each input QCN is translated to an equivalent QCN; in the second one, the QCNs are translated to approximations. These approaches take advantage of two dual notions that we introduce, the ones of refinement and abstraction between qualitative formalisms.