Abstract
Proposals for automating the creation of teaching materials across the sciences and humanities include question generation from ontologies. Those efforts have focused on multiple-choice questions, whereas learners also need to be exposed to other types of questions, such as yes/no and short answer questions. Initial results showed it is possible to create ontology-based questions. It is unknown how that can be done automatically and whether it would work beyond that use case in biology. We investigated this for ten types of educationally useful questions with additional sentence formulation variants. Each type of questions has a set of template specifications, axiom prerequisites on the ontology, and an algorithm to generate the questions from the ontology. Three approaches were designed: template variables using foundational ontology categories, using main classes from the domain ontology, and sentences mostly driven by natural language generation techniques. The user evaluation showed that the second approach resulted in slightly better quality questions than the first, and the linguistic-driven templates far outperformed both on syntactic and semantic adequacy of the questions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abacha, A.B., Dos Reis, J.C., Mrabet, Y., Pruski, C., Da Silveira, M.: Towards natural language question generation for the validation of ontologies and mappings. J. Biomed. Semant. 7(1), 1–15 (2016)
Alsubait, T., Parsia, B., Sattler, U.: Ontology-based multiple choice question generation. KI - Künstliche Intelligenz 30(2), 183–188 (2016)
Beisswanger, E., Schulz, S., Stenzhorn, H., Hahn, U.: BioTop: an upper domain ontology for the life sciences. Appl. Ontol. 3(4), 205–212 (2008)
Bouayad-Agha, N., Casamayor, G., Wanner, L.: Natural language generation in the context of the semantic web. Semant. Web 5(6), 493–513 (2014)
Bühmann, L., Usbeck, R., Ngonga Ngomo, A.-C.: ASSESS—automatic self-assessment using linked data. In: Arenas, M., et al. (eds.) The Semantic Web - ISWC 2015: 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11–15, 2015, Proceedings, Part II, pp. 76–89. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25010-6_5
Chaudhri, V.K., Clark, P.E., Overholtzer, A., Spaulding, A.: Question generation from a knowledge base. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) Knowledge Engineering and Knowledge Management: 19th International Conference, EKAW 2014, Linköping, Sweden, November 24–28, 2014. Proceedings, pp. 54–65. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13704-9_5
EV, V., Kumar, P.S.: Automated generation of assessment tests from domain ontologies. Semant. Web 8(6), 1023–1047 (2017)
Gatt, A., Reiter, E.: SimpleNLG: a realisation engine for practical applications. In: Proceedings of the ENLG 2009, pp. 90–93 (2009)
Graesser, A.C., Person, N., Huber, J.: Mechanisms that generate questions. Quest. Inf. Syst. 2, 167–187 (1992)
Hovy, E., Gerber, L., Hermjakob, U., Junk, M., Lin, C.Y.: Question answering in Webclopedia. In: Proceedings of the 9th Text retrieval conference (TREC-9) (2001)
Italian Ministry of Cultural Heritage and Activities: Italian institute of cognitive sciences and technologies, cultural-on (cultural ontology): cultural institute/site and cultural event ontology (2016). http://dati.beniculturali.it/cis/3.2
Keet, C.M.: A core ontology of macroscopic stuff. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds.) EKAW 2014. LNCS (LNAI), vol. 8876, pp. 209–224. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13704-9_17
Keet, C.M.: The African wildlife ontology tutorial ontologies. J. Biomed. Semant. 11, 1–4 (2020)
Khodeir, N.A., Elazhary, H., Wanas, N.: Generating story problems via controlled parameters in a web-based intelligent tutoring system. Int. J. Inf. Learn. Technol. 35(3), 199–216 (2018)
Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A.: Ontology library. WonderWeb Deliverable D18 (ver. 1.0, 31-12-2003) (2003). http://wonderweb.semanticweb.org
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Ngomo, A.C.N., Moussallem, D., Bühmann, L.: A holistic natural language generation framework for the semantic web. arXiv preprint arXiv:1911.01248 (2019)
Olney, A.M., Graesser, A.C., Person, N.K.: Question generation from concept maps. Dialogue Discourse 3(2), 75–99 (2012)
Papasalouros, A., Kanaris, K., Kotis, K.: Automatic generation of multiple choice questions from domain ontologies. In: Proceedings of the IADIS International Conference on e-learning, pp. 427–434 (2008)
Rodríguez Rocha, O., Faron Zucker, C.: Automatic generation of educational quizzes from domain ontologies. In: Proceedings of the EDULEARN, pp. 4024–4030 (2017)
Rodríguez Rocha, O., Faron Zucker, C.: Automatic generation of quizzes from DBpedia according to educational standards. In: The 3rd Educational Knowledge Management Workshop, Lyon, France, 23–27 April 2018, pp. 1035–1041 (2018)
Sirithumgul, P., Prasertsilp, P., Suksa-ngiam, W., Olfman, L.: An ontology-based framework as a foundation of an information system for generating multiple-choice questions. In: Proceedings of the 25th AMCIS (2019)
Vinu, E.V., Sreenivasa Kumar, P.: A novel approach to generate MCQs from domain ontology: considering Dl semantics and open-world assumption. J. Web Semant. 34, 40–54 (2015)
Zhang, L., VanLehn, K.: How do machine-generated questions compare to human-generated questions? Res. Pract. Technol. Enhanc. Learn. 11(1), 1–28 (2016). https://doi.org/10.1186/s41039-016-0031-7
Acknowledgements
TR acknowledges support from the Hasso Plattner Institute for Digital Engineering through the HPI Research School at UCT.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Raboanary, T., Wang, S., Keet, C.M. (2022). Generating Answerable Questions from Ontologies for Educational Exercises. In: Garoufallou, E., Ovalle-Perandones, MA., Vlachidis, A. (eds) Metadata and Semantic Research. MTSR 2021. Communications in Computer and Information Science, vol 1537. Springer, Cham. https://doi.org/10.1007/978-3-030-98876-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-98876-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98875-3
Online ISBN: 978-3-030-98876-0
eBook Packages: Computer ScienceComputer Science (R0)