Probabilistic Optimization of Semantic Process Model Matching

  • Henrik Leopold
  • Mathias Niepert
  • Matthias Weidlich
  • Jan Mendling
  • Remco Dijkman
  • Heiner Stuckenschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7481)


Business process models are increasingly used by companies, often yielding repositories of several thousand models. These models are of great value for business analysis such as service identification or process standardization. A problem is though that many of these analyses require the pairwise comparison of process models, which is hardly feasible to do manually given an extensive number of models. While the computation of similarity between a pair of process models has been intensively studied in recent years, there is a notable gap on automatically matching activities of two process models. In this paper, we develop an approach based on semantic techniques and probabilistic optimization. We evaluate our approach using a sample of admission processes from different universities.


Semantic Similarity Weak Order Business Process Model Business Object Admission Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Henrik Leopold
    • 1
  • Mathias Niepert
    • 2
  • Matthias Weidlich
    • 3
  • Jan Mendling
    • 4
  • Remco Dijkman
    • 5
  • Heiner Stuckenschmidt
    • 2
  1. 1.Humboldt-Universität zu BerlinBerlinGermany
  2. 2.Universität MannheimMannheimGermany
  3. 3.Technion - Israel Institute of TechnologyHaifaIsrael
  4. 4.Wirtschaftsuniversität WienViennaAustria
  5. 5.Eindhoven University of TechnologyEindhovenThe Netherlands

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