Macquarie University
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An ontology-based question-answering framework for adaptive e-learning systems

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posted on 2022-03-28, 14:09 authored by Sudath Rohitha Heiyanthuduwage
Current e-learning systems are based on legacy database systems. Databases focus on logically organising data, but not on conceptual knowledge. Therefore, current e-learning systems have limitations in retrieving domain knowledge from a legacy database and benefiting from the full expressive capabilities which an ontology-based approach would be able to provide. In order to alleviate this problem, we propose to augment current e-learning systems with Semantic Web Technologies. Furthermore, different educational institutions have similarities, yet an analysis we conducted on subject descriptors and course handbooks from a set of institutions elicits the terminological differences they use. Such differences hinder deploying an e-learning system in multiple institutions. We propose a twofold intuitive solution. Firstly, we propose an ontology-based plug and play architecture for developing an e-learning system's framework, instances of which can be deployed at different institutions by plugging in institution-specific learning ontologies. Secondly, we propose institution-specific learning ontologies for representing the knowledge and terminology specific to each institution. Developing institution-specific ontologies duplicates the effort of an ontology engineer. We analyse a corpus of learning ontologies developed for the learning domain. The results of our analysis show that the language constructors used in the corpus of the learning ontologies belong to a sublanguage of the ontology language OWL 2, that we name as OWL 2 Learn profile. We demonstrate how we use the OWL 2 Learn profile as a guide to developing an institution-specific ontology and populating it by mapping the data available in a learning database into instances and properties in an ontology. We develop an ontology benchmark query suite for evaluating learning ontologies for their query-answering and inference capabilities. We use the benchmark query suite to evaluate the sample learning ontologies that validate the expressivity of OWL 2 Learn profile as well. The adaptability of the e-learning system's framework is evaluated using a proof of concept prototype. We demonstrate how we can use two instances of the prototype with institution-specific ontologies for query-answering that generates similar answers, yet with domain specific terminology. This thesis shows how we can assist the development of ontology-based e-learning systems using Semantic Web technologies. We anticipate that this research would give some directions in developing context specific ontology-based e-learning systems.


Table of Contents

Chapter 1: Introduction -- Chapter 2: Related work on ontology-based e-learning systems -- Chapter 3: A plug and play framework and architecture for ontology-based adaptive e-learning systems -- Chapter 4: OWL 2 Learn profile: An ontology sublanguage for the learning domain -- Chapter 5: An approach for developing a learning ontology -- Chapter 6: Mapping and populating a learning ontology from a legacy database -- Chapter 7: Evaluating OWL 2 Learn ontologies and the framework for ontology-based e-learning systems -- Chapter 8: Conclusions.


Bibliography: pages 185-199 Theoretical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


PhD, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Mehmet A. Orgun


Copyright Sudath Rohitha Heiyanthuduwage 2018 Copyright disclaimer:




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