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.

@incollection{FS04KimD, title={Handling Granularity Differences in Knowledge Integration}, author={Doo Soon Kim and Bruce Porter}, booktitle={AAAI Fall Symposium on Computational Approaches to Representation Change during Learning and Development}, url="http://www.cs.utexas.edu/users/ai-lab?FS04KimD", year={2007} }