Skip to main content
SpringerLink
Log in

CLaRO: A Controlled Language for Authoring Competency Questions

  • Conference paper
  • First Online:
Book cover Metadata and Semantic Research (MTSR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1057))

Included in the following conference series:

Abstract

Competency Questions (CQs) assist in the development and maintenance of ontologies and similar knowledge organisation systems. The absence of tools to support the authoring of CQs has hampered their effective use. The few existing question templates have limited coverage of sentence constructions and are restricted to OWL. We aim to address this by proposing the CLaRO template-based CNL to author CQs. For its design, we exploited a new dataset of 234 CQs that had been processed automatically into 106 patterns, which we analysed and used to design a template-based CNL, with an additional CNL model and XML serialisation. The CNL was evaluated, showing coverage of about 90% with the 93 templates and their 41 variants. CLaRO has the potential to facilitate streamlining formalising ontology content requirements and, given that about one third of the CQs in the test sets turned out to be invalid questions, assist in writing good questions.

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

Access this chapter

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

Institutional subscriptions

Notes

  1. 1.

    https://github.com/CQ2SPARQLOWL/Dataset.

  2. 2.

    http://vicinity.iot.linkeddata.es/vicinity/; last accessed: 20 Dec. 2018.

  3. 3.

    page 4 of http://studentnet.cs.manchester.ac.uk/pgt/2014/COMP60421/slides/Week2-CQ.pdf; last accessed: 9-1-2019.

References

  1. Azzaoui, K., Jacoby, E., Senger, S., et al.: Scientific competency questions as the basis for semantically enriched open pharmacological space development. Drug Discov. Today 18(17), 843–852 (2013)

    Article  Google Scholar 

  2. Bezerra, C., Freitas, F.: Verifying description logic ontologies based on competency questions and unit testing. In: ONTOBRAS, pp. 159–164 (2017)

    Google Scholar 

  3. Bezerra, C., Freitas, F., Santana, F.: Evaluating ontologies with competency questions. In: Proceedings of IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) 2013, pp. 284–285. IEEE Computer Society, Washington, DC (2013)

    Google Scholar 

  4. Bezerra, C., Santana, F., Freitas, F.: CQChecker: a tool to check ontologies in OWL-DL using competency questions written in controlled natural language. Learn. Nonlinear Models 12(2), 4 (2014)

    Article  Google Scholar 

  5. Dasiopoulou, S., Meditskos, G., Efstathiou, V.: Semantic knowledge structures and representation. Technical report. D5.1, FP7-288199 Dem@Care: Dementia Ambient Care: Multi-Sensing Monitoring for Intelligence Remote Management and Decision Support. http://www.demcare.eu/downloads/D5.1SemanticKnowledgeStructures_andRepresentation.pdf

  6. Dennis, M., van Deemter, K., Dell’Aglio, D., Pan, J.Z.: Computing authoring tests from competency questions: experimental validation. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 243–259. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_15

    Chapter  Google Scholar 

  7. Hallett, C., Power, R., Scott, D.: Composing questions through conceptual authoring. Comput. Linguist. 33(1), 105–133 (2007)

    Article  Google Scholar 

  8. Hellmann, S., Lehmann, J., Auer, S., Brümmer, M.: Integrating NLP using linked data. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 98–113. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41338-4_7

    Chapter  Google Scholar 

  9. Keet, C.M.: Natural language template selection for temporal constraints. In: CREOL, JOWO 2017, vol. 2050, p. 12, Bolzano, Italy, 21–23 September 2017. CEUR-WS (2017)

    Google Scholar 

  10. Keet, C.M., Ławrynowicz, A.: Test-driven development of ontologies. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 642–657. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34129-3_39

    Chapter  Google Scholar 

  11. Kuhn, T.: A survey and classification of controlled natural languages. Comput. Linguist. 40(1), 121–170 (2014)

    Article  Google Scholar 

  12. Lyon, T.D., Saywitz, K.J., Kaplan, D.L., Dorado, J.S.: Reducing maltreated children’s reluctance to answer hypothetical oath-taking competency questions. Law Hum Behav. 25(1), 81–92 (2001)

    Article  Google Scholar 

  13. Malheiros, Y., Freitas, F.: A method to develop description logic ontologies iteratively based on competency questions: an implementation. In: ONTOBRAS, pp. 142–153 (2013)

    Google Scholar 

  14. Malone, J., et al.: The software ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation. J. Biomed. Sem. 5(1), 25 (2014)

    Article  Google Scholar 

  15. Moreira, J., Pires, L.F., van Sinderen, M., Daniele, L.: SAREF4health: IoT standard-based ontology-driven healthcare systems. In: Proceedings of FOIS 2018. FAIA, vol. 306, pp. 239–252. IOS Press (2018)

    Google Scholar 

  16. Mossakowski, T., Codescu, M., Neuhaus, F., Kutz, O.: The distributed ontology, modeling and specification language – DOL. In: Koslow, A., Buchsbaum, A. (eds.) The Road to Universal Logic. SUL, pp. 489–520. Birkhäuser, Cham (2015). https://doi.org/10.1007/978-3-319-15368-1_21

    Chapter  MATH  Google Scholar 

  17. Ren, Y., Parvizi, A., Mellish, C., Pan, J.Z., van Deemter, K., Stevens, R.: Towards competency question-driven ontology authoring. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 752–767. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_50

    Chapter  Google Scholar 

  18. Safwat, H., Davis, B.: CNLs for the semantic web: a state of the art. Lang. Resour. Eval. 51(1), 191–220 (2017)

    Article  Google Scholar 

  19. Salgueiro, A.M., Alves, C.B., Balsa, J.: Querying an ontology using natural language. In: Villavicencio, A., et al. (eds.) PROPOR 2018. LNCS (LNAI), vol. 11122, pp. 164–169. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99722-3_17

    Chapter  Google Scholar 

  20. Suarez-Figueroa, M.C., de Cea, G.A., Buil, C., et al.: NeOn methodology for building contextualized ontology networks. NeOn Deliverable D5.4.1, NeOn Project (2008)

    Google Scholar 

  21. Thiéblin, E., Haemmerlé, O., Trojahn, C.: Complex matching based on competency questions for alignment: a first sketch. In: 13th International Workshop on Ontology Matching (OM 2018), pp. 66–70. CEUR-WS, Monterey (2018)

    Google Scholar 

  22. Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)

    Article  Google Scholar 

  23. Williams, P.: Resourcing for the future? Information technology provision and competency questions for school-based initial teacher education. J. Inf. Technol. Teach. Educ. 5(3), 271–282 (1996)

    Google Scholar 

  24. Wisniewski, D., Potoniec, J., Lawrynowicz, A., Keet, C.M.: Competency questions and SPARQL-OWL queries dataset and analysis. Technical report 1811.09529, November 2018. https://arxiv.org/abs/1811.09529

  25. Zemmouchi-Ghomari, L., Ghomari, A.R.: Translating natural language competency questions into SPARQL queries: a case study. In: First International Conference on Building and Exploring Web Based Environments, pp. 81–86. IARIA (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cape Town, Cape Town, South Africa

    C. Maria Keet, Zola Mahlaza & Mary-Jane Antia

Authors
  1. C. Maria Keet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zola Mahlaza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mary-Jane Antia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Maria Keet .

Editor information

Editors and Affiliations

  1. International Hellenic University, Thessaloniki, Greece

    Emmanouel Garoufallou

  2. Guglielmo Marconi University, Rome, Italy

    Francesca Fallucchi

  3. Georg Eckert Institute – Leibniz Institute for International Textbook Research, Braunschweig, Germany

    Ernesto William De Luca

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Keet, C.M., Mahlaza, Z., Antia, MJ. (2019). CLaRO: A Controlled Language for Authoring Competency Questions. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36599-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36598-1

  • Online ISBN: 978-3-030-36599-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation