Skip to main content

Supporting Collaborative Modeling via Natural Language Processing

  • Conference paper
  • First Online:
Conceptual Modeling (ER 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12400))

Included in the following conference series:

Abstract

Engineering large-scale systems requires the collaboration among experts who use different modeling languages and create multiple models. Due to their independent creation and evolution, these models may exhibit discrepancies in terms of the domain concepts they represent. To help re-align the models without an explicit synchronization, we propose a technique that provides the modelers with suggested concepts that they may be interested in adding to their own models. The approach is modeling-language agnostic since it processes only the text in the models, such as the labels of elements and relationships. In this paper, we focus on determining the similarity of compound nouns, which are frequently used in conceptual models. We propose two algorithms, that make use of word embeddings and domain models, respectively. We report an early validation that assesses the effectiveness of our similarity algorithms against state-of-the-art machine learning algorithms with respect to human judgment.

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

Access this chapter

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

Institutional subscriptions

Notes

  1. 1.

    https://www.sesarju.eu/projects/pacas.

  2. 2.

    https://pacas.disi.unitn.it/pacas.

  3. 3.

    https://github.com/AndriyMulyar/semantic-text-similarity.

References

  1. van der Aa, H., Leopold, H., Reijers, H.A.: Comparing textual descriptions to process models - the automatic detection of inconsistencies. Inf. Syst. 64, 447–460 (2017)

    Article  Google Scholar 

  2. Aydemir, F.B., Dalpiaz, F.: Towards aligning multi-concern models via NLP. In: Proceedings of the MoDRE-RE (2017)

    Google Scholar 

  3. Aydemir, F.B., Dalpiaz, F.: Online appendix: supporting collaborative modelling via NLP. Figshare. https://figshare.com/s/e4a13da8404bcb74e0a0

  4. Beheshti, S.-M.-R., et al.: Process matching techniques. In: Beheshti, S.-M.-R., et al. (eds.) Process Analytics, pp. 61–90. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25037-3_3

    Chapter  Google Scholar 

  5. Cer, D.M., Diab, M.T., Agirre, E., Lopez-Gazpio, I., Specia, L.: SemEval-2017 task 1: semantic textual similarity - multilingual and cross-lingual focused evaluation. CoRR abs/1708.00055 (2017). http://arxiv.org/abs/1708.00055

  6. Claasen, R.: SimCom: measuring similarity of compound terms (2017), B.Sc. Thesis. https://github.com/RELabUU/concept-suggestor

  7. Clarke, S., Baniassad, E.: Aspect-Oriented Analysis and Design. Addison-Wesley, Boston (2005)

    Google Scholar 

  8. Curran, J.R.: From distributional to semantic similarity (2004)

    Google Scholar 

  9. Debreceni, C., Bergmann, G., Ráth, I., Varró, D.: Property-based locking in collaborative modeling. In: Proceedings of MODELS, pp. 199–209 (2017)

    Google Scholar 

  10. Delfmann, P., Herwig, S., Lis, L.: Unified enterprise knowledge representation with conceptual models-capturing corporate language in naming conventions. In: ICIS, p. 45 (2009)

    Google Scholar 

  11. Euzenat, J., Shvaiko, P.: Ontology Matching, vol. 18. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-49612-0

    Book  MATH  Google Scholar 

  12. Fill, H.G., Karagiannis, D.: On the conceptualisation of modelling methods using the ADOxx meta modelling platform. Enterp. Model. Inf. Syst. Archit. 8(1), 4–25 (2015)

    Article  Google Scholar 

  13. Fischer, K., Panfilenko, D., Krumeich, J., Born, M., Desfray, P.: Viewpoint-based modeling-towards defining the viewpoint concept and implications for supporting modeling tools. In: Proceedings of EMISA, pp. 123–136 (2012)

    Google Scholar 

  14. France, R., Ray, I., Georg, G., Ghosh, S.: Aspect-oriented approach to early design modelling. IEE Proc.-Softw. 151(4), 173–185 (2004)

    Article  Google Scholar 

  15. Gal, A.: Uncertain schema matching. Synth. Lect. Data Manag. 3(1), 1–97 (2011)

    Article  MathSciNet  Google Scholar 

  16. Grammel, B., Kastenholz, S., Voigt, K.: Model matching for trace link generation in model-driven software development. In: Proceedings of MODELS, pp. 609–625 (2012)

    Google Scholar 

  17. Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: Semantic similarity from natural language and ontology analysis. Synth. Lect. Hum. Lang. Technol. 8(1), 1–254 (2015)

    Article  Google Scholar 

  18. Kenter, T., De Rijke, M.: Short text similarity with word embeddings. In: Proceedings of CIKM, pp. 1411–1420 (2015)

    Google Scholar 

  19. Kögel, S., Groner, R., Tichy, M.: Automatic change recommendation of models and meta models based on change histories. In: Proceedings of ME@MODELS, pp. 14–19 (2016)

    Google Scholar 

  20. Lapata, M.: The disambiguation of nominalizations. Comput. Linguist. 28(3), 357–388 (2002)

    Article  Google Scholar 

  21. Leopold, H., van der Aa, H., Offenberg, J., Reijers, H.A.: Using hidden Markov models for the accurate linguistic analysis of process model activity labels. Inf. Syst. 83, 30–39 (2019)

    Article  Google Scholar 

  22. Levi, J.N.: The Syntax and Semantics of Complex Nominals. Academic Press, Cambridge (1978)

    Google Scholar 

  23. Lucassen, G., Robeer, M., Dalpiaz, F., van der Werf, J.M.E.M., Brinkkemper, S.: Extracting conceptual models from user stories with visual narrator. Requir. Eng. 22(3), 339–358 (2017). https://doi.org/10.1007/s00766-017-0270-1

    Article  Google Scholar 

  24. Nicolaescu, P., Rosenstengel, M., Derntl, M., Klamma, R., Jarke, M.: Near real-time collaborative modeling for view-based web information systems engineering. Inf. Syst. 74, 23–39 (2018)

    Article  Google Scholar 

  25. Northrop, L., et al.: Ultra-large-scale systems: the software challenge of the future. Technical report, Software Engineering Institute, Carnegie Mellon University (2006)

    Google Scholar 

  26. Nuseibeh, B., Kramer, J., Finkelsteiin, A.: Viewpoints: meaningful relationships are difficult! In: Proceedings of ICSE, pp. 676–681 (2003)

    Google Scholar 

  27. Sànchez-Ferreres, J., Carmona, J., Padró, L.: Aligning textual and graphical descriptions of processes through ILP techniques. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 413–427. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_26

    Chapter  Google Scholar 

  28. Séaghdha, D.O., Copestake, A.: Using lexical and relational similarity to classify semantic relations. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 621–629 (2009)

    Google Scholar 

  29. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2011)

    Article  Google Scholar 

  30. Sommerville, I., et al.: Large-scale complex IT systems. Commun. ACM 55(7), 71 (2012)

    Article  Google Scholar 

  31. Trask, A., Michalak, P., Liu, J.: sense2vec - a fast and accurate method for word sense disambiguation in neural word embeddings. CoRR abs/1511.06388 (2015)

    Google Scholar 

  32. Turney, P.D.: Similarity of semantic relations. Comput. Linguist. 32(3), 379–416 (2006)

    Article  Google Scholar 

  33. Viyović, V., Maksimović, M., Perisić, B.: Sirius: a rapid development of DSM graphical editor. In: Proceedings of INES, pp. 233–238 (2014)

    Google Scholar 

  34. Whittle, J., Jayaraman, P., Elkhodary, A., Moreira, A., Araújo, J.: MATA: a unified approach for composing UML aspect models based on graph transformation. In: Transactions on Aspect-Oriented Software Development VI, pp. 191–237 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatma Başak Aydemir .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aydemir, F.B., Dalpiaz, F. (2020). Supporting Collaborative Modeling via Natural Language Processing. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds) Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12400. Springer, Cham. https://doi.org/10.1007/978-3-030-62522-1_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-62522-1_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62521-4

  • Online ISBN: 978-3-030-62522-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics