Knowledge integration is a process of combining two different knowledge representations together. This task is important especially in learning where new information is combined with prior knowledge or in understanding where a coherent knowledge representation should be generated out of several knowledge fragments. A challenging problem in KI is handling granularity differences, i.e. combining together two knowledge representations with granularity differences. This paper presents an algorithm to find such correspondences between two representations with a granularity difference and to combine the two representations together based on the correspondences. The algorithm uses coarsening operators which generate coarse-grained representations from a representation. At the end, we introduce a large scale project in which the algorithm will be used.
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In AAAI Fall Symposium on Computational Approaches to Representation Change during Learning and Development 2007.
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Doo Soon Kim Ph.D. Alumni onue5 [at] cs utexas edu
Bruce Porter Faculty porter [at] cs utexas edu