Skip to main content
SpringerLink
Log in
Book cover

European Semantic Web Conference

ESWC 2020: The Semantic Web: ESWC 2020 Satellite Events pp 240–249Cite as

Semantic Parsing of Textual Requirements

  • Conference paper
  • First Online:

  • 818 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12124))

Abstract

Requirements are critical components in the industry, describing qualities that a product or a service needs to have. Most requirements are only available as natural language text embedded in a document. Working with textual requirements is getting increasingly difficult due to the growing number of requirements, and having the requirements available as structured data would be beneficial. However, the work required for the translation of natural language requirements into structured data is daunting. Thus, we need tools to aid in this process. In this Ph.D. project, we propose to use state-of-the-art knowledge extraction techniques and develop novel methods to identify the terms and relationships in a requirement and align them with an existing domain-ontology. To achieve this goal, we must overcome the difficulties in working with both domain-specific technical corpora and ontologies. Furthermore, existing tools and NLP models must be adapted to the domain.

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.

    The annotation is available at https://gitlab.com/oholter/scd-annotations.

References

  1. Abualhaija, S., Arora, C., et al.: A Machine learning-based approach for demarcating requirements in textual specifications, pp. 51–62. In: RE (2019)

    Google Scholar 

  2. Betteridge, J., Carlson, A., et al.: Toward never ending language learning. In: AAAI Spring Symposium: Learning by Reading and Learning to Read, pp. 1–2 (2009)

    Google Scholar 

  3. Casamayor, A., Godoy, D., Campo, M.: Identification of non-functional requirements in textual specifications: a semi-supervised learning approach. Inf. Softw. Technol. 52(4), 436–445 (2010)

    Google Scholar 

  4. Christel, M.G., Kang, K.C.: Issues in requirements elicitation. Carnegie-Mellon University Pittsburgh PA Software Engineering Institute (1992)

    Google Scholar 

  5. DNV GL: DNV-RU-SHIP-Pt4-Ch7: Rules for classification - Ships. https://rules.dnvgl.com/docs/pdf/DNVGL/RU-SHIP/2018-01/DNVGL-RU-SHIP-Pt4Ch7.pdf. Accessed 07 Feb 2020

  6. Drouin, P.: Term extraction using non-technical corpora as a point of leverage. Terminology 9(1), 99–115 (2003)

    Google Scholar 

  7. Etzioni, O., Banko, M., Cafarella, M.J.: Machine reading. In: AAAI, vol. 6, pp. 1517–1519 (2006)

    Google Scholar 

  8. Gangemi, A., Presutti, V., Recupero, R., et al.: Semantic web machine reading with FRED. Semant. Web 8(6), 873–893 (2017)

    Article  Google Scholar 

  9. Gardner, M., Grus, J., et al.: AllenNLP: a deep semantic natural language processing platform. arXiv:1803.07640 (2018)

  10. Grishman, R., Sundheim, B.: Message understanding conference-6: a brief history, p. 6. In: COLING (1996)

    Google Scholar 

  11. International Council on Systems Engineering: INCOSE. https://www.incose.org/. Accessed 21 Jan 2020

  12. Jacquemin, C., Bourigault, D.: Term Extraction and Automatic Indexing, vol. 1. Oxford University Press, Oxford (2012)

    Google Scholar 

  13. Judea, A., Schutze, H., Bruegmann, S.: Unsupervised training set generation for automatic acquisition of technical terminology in patents, p. 11. In: COLING (2014)

    Google Scholar 

  14. Klüwer, J.W., Waaler, A.: Reified requirements ontology (2019). https://w3id.org/requirement-ontology/ontology/core/A01A. Accessed 30 Jan 2020

  15. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv:1603.01360 (2016)

  16. Lee, J.Y., Dernoncourt, F., et al.: Transfer learning for named-entity recognition with neural networks. arXiv:1705.06273 (2017)

  17. Nooralahzadeh, F., Lønning, J.T., Øvrelid, L.: Reinforcement-based denoising of distantly supervised NER with partial annotation, pp. 225–233. In: DeepLo (2019)

    Google Scholar 

  18. Ratner, A., De Sa, C., Wu, S., Selsam, D., Ré, C.: Data programming: creating large training sets, quickly. In: NeurIPS (2016)

    Google Scholar 

  19. Rigouts Terryn, A., Drouin, P., Hoste, V., Lefever, E.: Analysing the impact of supervised machine learning on automatic term extraction: HAMLET vs TermoStat, pp. 1012–1021. In: RANLP (2019)

    Google Scholar 

  20. Savova, G.K., Masanz, J.J., et al.: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17, 507–513 (2010)

    Article  Google Scholar 

  21. Seresht, S.M., Ormandjieva, O., Sabra, S.: Automatic conceptual analysis of user requirements with the requirements engineering assistance diagnostic (READ) tool, pp. 133–142. In: SERA (2008)

    Google Scholar 

  22. Siemens AG: Polarion ALM. https://polarion.plm.automation.siemens.com. Accessed 07 Jan 2020

  23. SIRIUS: DREAM and READI: Cooperation to Manage Digital Requirements. https://sirius-labs.no/dream-and-readi-cooperation-to-manage-digital-requirements/. Accessed 15 Nov 2019

  24. Sultanov, H., Hayes, J.H.: Application of reinforcement learning to requirements engineering: requirements tracing, pp. 52–61. In: RE (2013)

    Google Scholar 

  25. Wang, Y.: Automatic semantic analysis of software requirements through machine learning and ontology approach. J. Shanghai Jiaotong Univ. (Sci.) 21(6), 692–701 (2016). https://doi.org/10.1007/s12204-016-1783-3

    Article  Google Scholar 

  26. Winkler, J., Vogelsang, A.: Automatic classification of requirements based on convolutional neural networks. pp. 39–45. In: REW (2016)

    Google Scholar 

  27. Wu, S., He, Y.: Enriching pre-trained language model with entity information for relation classification. eprint arXiv:1905.08284 (2019)

  28. Zhao, Y., Wan, H., Gao, J., Lin, Y.: Improving relation classification by entity pair graph. In: Asian Conference on Machine Learning, pp. 1156–1171 (2019)

    Google Scholar 

Download references

Acknowledgements

The Ph.D. project is supervised by Basil Ell, Martin Giese, and Lilja Øvrelid and is funded by the SIRIUS centre (http://sirius-labs.no): Norwegian Research Council project number 237898. It is co-funded by partner companies, including DNV GL and Equinor.

Author information

Authors and Affiliations

  1. Department of Informatics, University of Oslo, Oslo, Norway

    Ole Magnus Holter

Authors
  1. Ole Magnus Holter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ole Magnus Holter .

Editor information

Editors and Affiliations

  1. University of Erlangen-Nuremberg, Nuremberg, Germany

    Andreas Harth

  2. National Research Council, Rome, Italy

    Valentina Presutti

  3. Eurecom, Sophia Antipolis, France

    Raphaël Troncy

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Maribel Acosta

  5. Institute for Information Business at WU Wien, Vienna, Austria

    Axel Polleres

  6. Institute for Information Business at WU Wien, Vienna, Austria

    Javier D. Fernández

  7. Siemens AG Österreich, Vienna, Austria

    Josiane Xavier Parreira

  8. Linköping University, Linköping, Sweden

    Olaf Hartig

  9. Aalborg University, Aalborg, Denmark

    Katja Hose

  10. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Michael Cochez

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

Holter, O.M. (2020). Semantic Parsing of Textual Requirements. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-62327-2_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62326-5

  • Online ISBN: 978-3-030-62327-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation