Handling Granularity Differences in Knowledge Integration (2007)
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.
In AAAI Fall Symposium on Computational Approaches to Representation Change during Learning and Development 2007.

Doo Soon Kim Ph.D. Alumni onue5 [at] cs utexas edu
Bruce Porter Faculty porter [at] cs utexas edu