Tahani Alsubait

Ontology-Based Multiple Choice Question Generation

Multiple choice questions (MCQs) are considered highly useful (being easy to take or mark) but quite difficult to create and large numbers are needed to form valid exams and associated practice materials. The idea of re-using an existing ontology to generate MCQs almost suggests itself and has been explored in various projects. In this project, we are applying suitable educational theory regarding assessments and related methods for their evaluation to ontology-based MCQ generation. In particular, we investigate whether we can measure the similarity of the concepts in an ontology with sufficient reliability so that this measure can be used to control the difficulty of the MCQs generated. In this report, we provide an overview of the background to this research, and describe the main steps taken and insights gained.

Next generation of e-assessment: automatic generation of questions

This paper provides a review of the state-of-the-art in automatic assessment generation. The paper focuses on and further develops methods for automatic generation of assessments from ontologies. We describe a novel approach and evaluate it by comparing it to other existing approaches. In addition, we report on our experience to evaluate the generated questions using a corpus-based method to simulate a real student trying to solve the questions.

Generating Multiple Choice Questions From Ontologies: How Far Can We Go?

Ontology-based Multiple Choice Question (MCQ) generation has a relatively short history. Many attempts have been carried out to develop methods to generate MCQs from ontologies. However, there is still a need to understand the applicability of these methods in real educational settings. In this paper, we present an empirical evaluation of ontology-based MCQ generation. We examine the feasibility of applying ontology-based MCQ generation methods by educators with no prior experience in ontology building. The findings of this study show that this is feasible and can result in generating a reasonable number of educationally useful questions with good predictions about their difficulty levels.

Measuring similarity in ontologies: a new family of measures

Several attempts have been already made to develop similarity measures for ontologies. We noticed that some existing similarity measures are ad-hoc and unprincipled. In addition, there is still a need for similarity measures which are applicable to expressive Description Logics and which are terminological. To address these requirements, we have developed a new family of similarity measures. Two separate empirical studies have been carried out to evaluate the new measures. First, we compare the new measures along with some existing measures against a gold-standard. Second, we examine the practicality of using the new measures over an independently motivated corpus of ontologies.

A Similarity Based Approach to Omission Finding in Ontologies

With the growing interest in using ontologies in semantically-enabled applications, the interest in enhancing the quality of such ontologies has grown as well. Standard reasoning services focus on certain obvious dimensions of quality, e.g., to detect inconsistencies and incoherence. In addition, bespoke tools have been presented to address the completeness dimension of quality, e.g., missing entailments. These tools are usually focused on very restricted subsets of all the possible missing entailments, i.e., only atomic subsumptions. We present a new protocol to detect both existing invalid entailments and missing valid entailments. We also present a case study to evaluate the usefulness of the presented protocol for ontology validation purposes.