Automatic Question Generation In Education Domain Based On Ontology

Generating questions at various difficulty levels require significant time and are challenging. It requires specific knowledge and skills. The quality of the generated questions would depend on the question maker's ability to relate information and represent it in the form of a question. Thus, it is difficult to maintain the quality consistency of a large set of questions. This study introduces an ontology-based approach for automating the generation of questions to maintain consistency of question quality. Information related to the ontology is broken down into information categories in the format of SPARQL queries. The queries are then converted into questions. Experts were asked to validate the generated questions. Based on our experiments, the accuracy of the generated questions reaches 86%.