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.