Abstract
It is common for large organizations to maintain repositories of business process models and model comparison happens when organizations merge or measure the gap between their own processes and industry-wide standards. Any comparison between process models relies on a construction of relationship between the elements of one model and the elements in the other model. To resolve this automatic construction issue, a three-step approach is proposed to align business process models based on lexical and structural matching to support discovering complex matches especially. The potential node matches, which are first identified by lexical and context similarity, are further grouped to potential complex matches according to the rules we defined. Then an extended graph structure based algorithm is used to select the optimum mapping in the potential matches. Finally, an experiment based on real-world process models from BPM AI is conducted to evaluate the effectiveness and efficiency of our approach.
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