Skip to main content
SpringerLink
Log in
Book cover

International Conference on Design Science Research in Information System and Technology

DESRIST 2017: Designing the Digital Transformation pp 21–38Cite as

Extracting Business Objects and Activities from Labels of German Process Models

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10243))

Abstract

To automatically analyze and compare elements of process models, investigating the natural language contained in the labels of the process models is inevitable. Therefore, the adaption of well-established techniques from the field of natural language processing to Business Process Management has recently experienced a growth. Our work contributes to the field of natural language processing in business process models by providing a word dependency-based technique for the extraction of business objects and activities from German labeled process models. Furthermore, we evaluate our approach by implementing it in the RefMod-Miner toolset and measuring the quality of the information extraction in business process models. In three different evaluation scenarios, we show the strengths of the dependency-based approach and give an outlook on how further research could benefit from the approach.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    cf. http://universaldependencies.org/.

  2. 2.

    cf. http://ufal.mff.cuni.cz/conll2009-st/task-description.html.

  3. 3.

    http://refmod-miner.dfki.de.

  4. 4.

    The available languages are maintained at http://universaldependencies.org.

  5. 5.

    cf. https://nlp.stanford.edu/software/lex-parser.shtml.

References

  1. Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, Pearson Education International, Upper Saddle River (2009)

    Google Scholar 

  2. Leopold, H., Smirnov, S., Mendling, J.: Recognising activity labeling styles in business process models. Enterp. Model. Inf. Syst. Architect. (EMISA) 6, 16–29 (2011)

    Google Scholar 

  3. Becker, M., Laue, R.: A comparative survey of business process similarity measures. Comput. Ind. 63, 148–167 (2012)

    Article  Google Scholar 

  4. Antunes, G., Bakhshandeh, M., Borbinha, J., Cardoso, J., Dadashnia, S., Di Francescomarino, C., Dragoni, M., Fettke, P., Gal, A., Ghidini, C., Hake, P., Khiat, A., Klinkmüller, C., Kuss, E., Leopold, H., Loos, P., Meilicke, C., Niesen, T., Pesquita, C., Péus, T., Schoknecht, A., Sheetrit, E., Sonntag, A., Stuckenschmidt, H., Thaler, T., Weber, I., Weidlich, M.: The process model matching contest 2015. In: Kolb, J. (ed.) Enterprise Modelling and Information Systems Architectures, pp. 127–155. Gesellschaft für Informatik, Bonn (2015)

    Google Scholar 

  5. Leopold, H.: Natural Language in Business Process Models. Springer, Berlin (2013)

    Book  Google Scholar 

  6. Mendling, J., Reijers, H.A., Recker, J.: Activity labeling in process modeling: empirical insights and recommendations. Inf. Syst. 35, 467–482 (2010)

    Article  Google Scholar 

  7. Mendling, J., Reijers, H.A., van der Aalst, W.M.P.: Seven process modeling guidelines (7PMG). Inf. Softw. Technol. 52, 127–136 (2010)

    Article  Google Scholar 

  8. Leopold, H., Eid-Sabbaghb, R.-H., Mendling, J., Guerreiro Azevedod, L., Araujo Baiãod, F.: Detection of naming convention violations in process models for different languages. Decis. Support Syst. 56, 310–325 (2013)

    Article  Google Scholar 

  9. Sonntag, A., Hake, P., Fettke, P., Loos, P.: An Approach For Semantic Business Process Model Matching Using Supervised Machine Learning. Association for Information Systems (AIS) (2016)

    Google Scholar 

  10. Dijkman, R., Dumas, M., van Dongen, B., Käärik, R., Mendling, J.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36, 498–516 (2011)

    Article  Google Scholar 

  11. Bergner, M., Fill, H.-G., Johannsen, F.: Supporting business process improvement with natural language processing: a model-based approach. In: Mayr, H.C., Pinzger, M. (eds.) GI Informatik 2016, Klagenfurt, pp. 717–730 (2016)

    Google Scholar 

  12. Skersys, T., Butleris, R., Kapocius, K., Vileiniskis, T.: An approach for extracting business vocabularies from business process models. Inf. Technol. Control 42, 150–158 (2013)

    Google Scholar 

  13. Leopold, H., Smirnov, S., Mendling, J.: On the refactoring of activity labels in business process models. Inf. Syst. 37, 443–459 (2012)

    Article  Google Scholar 

  14. Volokh, A., Neumann, G.: Dependency parsing with efficient feature extraction. In: Glimm, B., Krüger, A. (eds.) KI 2012. LNCS, vol. 7526, pp. 253–256. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33347-7_26

    Chapter  Google Scholar 

  15. Chen, D., Manning, C.D.: A fast and accurate dependency parser using neural networks. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 740–750. Association for Computational Linguistics (2014)

    Google Scholar 

  16. Weichselbraun, A., Süsstrunk, N.: Optimizing dependency parsing throughput. In: 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), vol. 1, pp. 511–516 (2015)

    Google Scholar 

  17. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  18. Müller, T., Schütze, H.: Robust morphological tagging with word representations. In: North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (2015)

    Google Scholar 

  19. Benikova, D., Biemann, C., Kisselew, M., Padó, S.: Germeval 2014 named entity recognition shared task: companion paper. In: GermEval 2014 Named Entity Recognition Shared Task, vol. 7, p. 10 (2014)

    Google Scholar 

  20. Keller, G., Teufel, T.: SAP R/3 prozeßorientiert anwenden – Iteratives Prozeß-Prototyping zur Bildung von Wertschöpfungsketten. Addison-Wesley, Bonn (1998)

    Google Scholar 

  21. Becker, J., Schütte, R.: Handelsinformationssysteme. MI Wirtschaftsbuch (2004)

    Google Scholar 

  22. Hamp, B., Feldweg, H.: GermaNet - a lexical-semantic net for German. In: Vossen, P., Adriaens, G., Calzolari, N., Sanfilippo, A., Wilks, Y. (eds.) ACL/EACL Workshop, pp. 9–15. Association for Computer Linguistics, Madrid (1997)

    Google Scholar 

  23. Brants, S., Dipper, S., Eisenberg, P., Hansen-Schirra, S., König, E., Lezius, W., Rohrer, C., Smith, G., Uszkoreit, H.: TIGER: linguistic interpretation of a German corpus. Res. Lang. Comput. 2, 597–620 (2004)

    Article  Google Scholar 

  24. De Marneffe, M.-C., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., Manning, C.D.: Universal stanford dependencies: a cross-linguistic typology, pp. 4585–4592 (2014)

    Google Scholar 

  25. Rehse, J.-R., Hake, P., Fettke, P., Loos, P.: Inductive reference model development: recent results and current challenges. In: Mayr, H.C., Pinzger, M. (eds.) INFORMATIK 2016. Jahrestagung der Gesellschaft für Informatik (INFORMATIK-2016), vol. P-259. GI, Bonn/Klagenfurt (2016)

    Google Scholar 

  26. Universal Dependencies. http://universaldependencies.org

Download references

Acknowledgement

This research was funded in part by the German Federal Ministry of Education and Research under grant number 01IS12050 (project SemGo). The responsibility for this publication lies with the authors.

Author information

Authors and Affiliations

  1. Institute for Information Systems, Saarland University, Saarbrücken, Germany

    Philip Hake, Peter Fettke & Peter Loos

  2. German Research Center for Artificial Intelligence, Saarbrücken, Germany

    Philip Hake, Peter Fettke, Günter Neumann & Peter Loos

Authors
  1. Philip Hake
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Fettke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Günter Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Loos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philip Hake .

Editor information

Editors and Affiliations

  1. Karlsruhe Institute of Technologie (KIT) , Karlsruhe, Germany

    Alexander Maedche

  2. University of Liechtenstein , Vaduz, Liechtenstein

    Jan vom Brocke

  3. University of South Florida , Tampa, Florida, USA

    Alan Hevner

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Hake, P., Fettke, P., Neumann, G., Loos, P. (2017). Extracting Business Objects and Activities from Labels of German Process Models. In: Maedche, A., vom Brocke, J., Hevner, A. (eds) Designing the Digital Transformation. DESRIST 2017. Lecture Notes in Computer Science(), vol 10243. Springer, Cham. https://doi.org/10.1007/978-3-319-59144-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59144-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59143-8

  • Online ISBN: 978-3-319-59144-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation