Towards the Automated Annotation of Process Models

  • Henrik Leopold
  • Christian Meilicke
  • Michael Fellmann
  • Fabian Pittke
  • Heiner Stuckenschmidt
  • Jan Mendling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9097)

Abstract

Many techniques for the advanced analysis of process models build on the annotation of process models with elements from predefined vocabularies such as taxonomies. However, the manual annotation of process models is cumbersome and sometimes even hardly manageable taking the size of taxonomies into account. In this paper, we present the first approach for automatically annotating process models with the concepts of a taxonomy. Our approach builds on the corpus-based method of second-order similarity, different similarity functions, and a Markov Logic formalization. An evaluation with a set of 12 process models consisting of 148 activities and the PCF taxonomy consisting of 1,131 concepts demonstrates that our approach produces satisfying results.

Keywords

Process model Taxonomy Automatic annotation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    APQC. Apqc process classification framework (pcf) - cross industry - pdf version 5.2.0. Technical report (2012)Google Scholar
  2. 2.
    Becker, J., Bergener, P., Räckers, M., Weiß, B., Winkelmann, A.: Pattern-based semi-automatic analysis of weaknesses in semantic business process models in the banking sector (2010)Google Scholar
  3. 3.
    Bögl, A., Schrefl, M., Pomberger, G., Weber, N.: Semantic annotation of EPC models in engineering domains to facilitate an automated identification of common modelling practices. In: Filipe, J., Cordeiro, J. (eds.) Enterprise Information Systems. LNBIP, vol. 19, pp. 155–171. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  4. 4.
    Born, M., Dörr, F., Weber, I.: User-friendly semantic annotation in business process modeling. In: Weske, M., Hacid, M.-S., Godart, C. (eds.) WISE Workshops 2007. LNCS, vol. 4832, pp. 260–271. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  5. 5.
    Cai, Z., McNamara, D.S., Louwerse, M., Hu, X., Rowe, M., Graesser, A.C.: Nls: A non-latent similarity algorithm. In: Proc. 26th Ann. Meeting of the Cognitive Science Soc. (CogSci 2004), pp. 180–185 (2004)Google Scholar
  6. 6.
    Cayoglu, U., Dijkman, R., Dumas, M., Fettke, P., Garcıa-Banuelos, L., Hake, P., Klinkmüller, C., Leopold, H., Ludwig, A., Loos, P., et al.: The process model matching contest 2013. In: 4th International Workshop on Process Model Collections: Management and Reuse (PMC-MR 2013) (2013)Google Scholar
  7. 7.
    Di Francescomarino, C., Tonella, P.: Supporting ontology-based semantic annotation of business processes with automated suggestions. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) Enterprise, Business-Process and Information Systems Modeling. LNBIP, vol. 29, pp. 211–223. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  8. 8.
    Ehrig, M., Euzenat, J., et al.: Relaxed precision and recall for ontology matching. In: Proc. K-Cap 2005 workshop on Integrating ontology, pp. 25–32 (2005)Google Scholar
  9. 9.
    Governatori, G., Hoffmann, J., Sadiq, S., Weber, I.: Detecting regulatory compliance for business process models through semantic annotations. In: Ardagna, D., Mecella, M., Yang, J. (eds.) Business Process Management Workshops. LNBIP, vol. 17, pp. 5–17. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Hofferer, P.: Achieving business process model interoperability using metamodels and ontologies (2007)Google Scholar
  11. 11.
    Islam, A., Inkpen, D.: Second order co-occurrence pmi for determining the semantic similarity of words. In: LREC, pp. 1033–1038 (2006)Google Scholar
  12. 12.
    Klinkmüller, C., Weber, I., Mendling, J., Leopold, H., Ludwig, A.: Increasing recall of process model matching by improved activity label matching. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 211–218. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  13. 13.
    Kolb, P.: Disco: a multilingual database of distributionally similar words. In: Proceedings of KONVENS-2008, Berlin (2008)Google Scholar
  14. 14.
    Leopold, H., Niepert, M., Weidlich, M., Mendling, J., Dijkman, R., Stuckenschmidt, H.: Probabilistic optimization of semantic process model matching. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 319–334. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  15. 15.
    Leopold, H., Smirnov, S., Mendling, J.: On the refactoring of activity labels in business process models. Information Systems 37(5), 443–459 (2012)CrossRefGoogle Scholar
  16. 16.
    Lin, Y., Strasunskas, D., Hakkarainen, S.E., Krogstie, J., Solvberg, A.: Semantic annotation framework to manage semantic heterogeneity of process models. In: Martinez, F.H., Pohl, K. (eds.) CAiSE 2006. LNCS, vol. 4001, pp. 433–446. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  17. 17.
    Malone, T.W., Crowston, K., Herman, G.A.: Organizing business knowledge: the MIT process handbook. MIT press (2003)Google Scholar
  18. 18.
    Mendling, J., Reijers, H.A., Recker, J.: Activity Labeling in Process Modeling: Empirical Insights and Recommendations. Information Systems 35(4), 467–482 (2010)CrossRefGoogle Scholar
  19. 19.
    Miller, G., Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998) Google Scholar
  20. 20.
    Noessner, J., Niepert, M., Stuckenschmidt, H.: Rockit: exploiting parallelism and symmetry for map inference in statistical relational models. Statistical Relational Artificial Intelligence. In: AAAI Workshop (2013)Google Scholar
  21. 21.
    Richardson, M., Domingos, P.: Markov logic networks. Machine learning 62(1–2), 107–136 (2006)CrossRefGoogle Scholar
  22. 22.
    Sadiq, W., Governatori, G., Namiri, K.: Modeling control objectives for business process compliance. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 149–164. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  23. 23.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval (1983)Google Scholar
  24. 24.
    Stephens, S.: Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice. Information Systems Frontiers 3(4), 471–476 (2001)CrossRefGoogle Scholar
  25. 25.
    Thomas, O., Fellmann, M.: Semantic Process Modeling - Design and Implementation of an Ontology-Based Representation of Business Processes. Business & Information Systems Engineering 1(6), 438–451 (2009)CrossRefGoogle Scholar
  26. 26.
    Weidlich, M., Dijkman, R., Mendling, J.: The ICoP framework: identification of correspondences between process models. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 483–498. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  27. 27.
    Wetzstein, B., Ma, Z., Filipowska, A., Kaczmarek, M., Bhiri, S., Losada, S., Lopez-Cob, J.-M., Cicurel, L.: Semantic business process management: a lifecycle based requirements analysis. In: SBPM (2007)Google Scholar
  28. 28.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Henrik Leopold
    • 1
  • Christian Meilicke
    • 2
  • Michael Fellmann
    • 3
  • Fabian Pittke
    • 4
  • Heiner Stuckenschmidt
    • 2
  • Jan Mendling
    • 4
  1. 1.VU University AmsterdamAmsterdamThe Netherlands
  2. 2.Universität MannheimMannheimGermany
  3. 3.Universität OsnabrückOsnabrückGermany
  4. 4.WU ViennaViennaAustria

Personalised recommendations