Towards the generalisation of the generation of answerable questions from ontologies for education. (31st May 2023) - Raboanary, Toky Hajatiana; Wang, Steve; Keet, C. Maria
Generating questions automatically from ontologies, and marking thereof, may support teaching and learning activities and therewith alleviate a teacher's workload. Numerous studies considered this for MCQs; however, learners also have to be confronted with, for instance, yes/no and short answer questions. We investigated ten types of educationally valuable questions. For each question type, we determined the axiom prerequisites to be able to generate and answer it and declared a set of template specifications as question sentence plans. Three algorithmic approaches were devised for generating the text from the ontology: semantics-based with 1) template variables using foundational ontology categories, or 2) using main classes from the domain ontology and 3) generation mostly driven by NLP techniques. User evaluation demonstrated that option three far outperformed the ontology-based ones on syntactic and semantic correctness of the generated questions, and it generated 98.45% of the questions from all valid axiom prerequisites in our experiment.