We describe a new model for representing semantic workflows as semantically labeled graphs, together with a related model for knowledge intensive similarity measures. The application of this model to scientific and business workflows is discussed. Experimental evaluations show that similarity measures can be modeled that are well aligned with manual similarity assessments. Further, new algorithms for workflow similarity computation based on A~* search are described. A new retrieval algorithm is introduced that goes beyond traditional sequential retrieval for graphs, interweaving similarity computation with case selection. Significant reductions on the overall retrieval time are demonstrated without sacrificing the correctness of the computed similarity.
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