Retrieval of Semantic Workflows with Knowledge Intensive Similarity Measures

  • Ralph Bergmann
  • Yolanda Gil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6880)


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.


Similarity Measure Aggregation Function Priority Queue Retrieval Performance Decision Tree Modeler 
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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  1. 1.
    Becker, J., Bergener, P., Breuker, D., Räckers, M.: On measures of behavioral distance between business processes. In: Proceedings of the 10th International Conference on Wirtschaftsinformatik, vol. 2, pp. 665–674 (2011)Google Scholar
  2. 2.
    Bergmann, R.: Experience Management. LNCS (LNAI), vol. 2432. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  3. 3.
    Bergmann, R., Freßmann, A., Maximini, K., Maximini, R., Sauer, T.: Case-based support for collaborative business. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 519–533. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Burkhard, H.D., Richter, M.M.: On the notion of similarity in case based reasoning and fuzzy theory. Soft Computing in Case Based Reasoning (2000)Google Scholar
  5. 5.
    Champin, P.A., Solnon, C.: Measuring the similarity of labeled graphs. CBR Research and Development, 1066–1067 (2003)Google Scholar
  6. 6.
    Cheng, W., Rademaker, M., Baets, B.D., Hüllermeier, E.: Predicting partial orders: ranking with abstention. Machine Learning and Knowledge Discovery in Databases, 215–230 (2010)Google Scholar
  7. 7.
    Chinthaka, E., Ekanayake, J., Leake, D., Plale, B.: CBR based workflow composition assistant. In: World Conference on Services-I, pp. 352–355 (2009)Google Scholar
  8. 8.
    Dijkman, R., Dumas, M., Garcia-Banuelos, L.: Graph matching algorithms for business process model similarity search. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 48–63. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Gil, Y., Kim, J., Florez, G., Ratnakar, V., González-Calero, P.A.: Workflow matching using semantic metadata. In: Proceedings of the 5th International Conference on Knowledge Capture, pp. 121–128 (2009)Google Scholar
  10. 10.
    Gil, Y., Ratnakar, V., Kim, J., González-Calero, P., Groth, P., Moody, J., Deelman, E.: Wings: Intelligent Workflow-Based design of computational experiments. IEEE Intelligent Systems 26(1), 62–72 (2011)CrossRefGoogle Scholar
  11. 11.
    Goderis, A., Li, P., Goble, C.: Workflow discovery: the problem, a case study from e-science and a graph-based solution. International Journal of Web Services Research 5(4) (2008)Google Scholar
  12. 12.
    Goderis, A.: Workflow Re-use and Discovery in Bioinformatics. Ph.D. thesis, University of Manchester (2008)Google Scholar
  13. 13.
    Hull, D., Zolin, E., Bovykin, A., Horrocks, I., Sattler, U., Stevens, R.: Deciding semantic matching of stateless services. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 1319 (2006)Google Scholar
  14. 14.
    Leake, D.B., Kendall-Morwick, J.: Towards Case-Based support for e-Science workflow generation by mining provenance. In: Althoff, K.D., Bergmann, R., Minor, M., Hanft, A. (eds.) Advances in CBR, pp. 269–283 (2008)Google Scholar
  15. 15.
    Madhusudan, T., Zhao, J.L., Marshall, B.: A case-based reasoning framework for workflow model management. Data & Knowledge Engineering 50(1), 87–115 (2004)CrossRefGoogle Scholar
  16. 16.
    Minor, M., Tartakovski, A., Bergmann, R.: Representation and structure-based similarity assessment for agile workflows. In: Weber, R., Richter, M.M. (eds.) CBR Research and Development, pp. 224–238 (2007)Google Scholar
  17. 17.
    Minor, M., Bergmann, R., Görg, S., Walter, K.: Towards Case-Based adaptation of workflows. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS (LNAI), vol. 6176, pp. 421–435. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Taylor, I.J., Deelman, E., Gannon, D.B.: Workflows for e-Science. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Weber, B., Wild, W., Breu, R.: CBRFlow: enabling adaptive workflow management through conversational Case-Based reasoning. In: Funk, P., Gonzlez-Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 434–448. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ralph Bergmann
    • 1
  • Yolanda Gil
    • 2
  1. 1.Department of Business Information Systems IIUniversity of TrierTrierGermany
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA

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