Saudi Cultural Missions Theses & Dissertations
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Item Restricted Generating Complex Questions from Domain Ontologies(University of Liverpool, 2025) Alkhuzaey, Samah; Payne, Terry; Tamma, Valentina; Grasso, FlorianaDesigning and constructing pedagogical tests that equitably measure various skills across different students is a challenging task. The quality and validity of assessments are heavily reliant on the quality of the questions included. Traditional test development methods rely on manual effort, which can be time-consuming, labour-intensive and inconsistent, leading to variability in question quality. This challenge is further compounded by the advent of online learning platforms that require a large and diverse pool of questions, making manual creation and review impractical. Furthermore, for effective assessments, questions must be calibrated with difficulty levels before being incorporated into exams. However, difficulty calibration is another challenge that complicates questions development. In recent years, Automatic Question Generation (AQG) has emerged as a powerful tool for effortlessly generating assessment questions in massive numbers with a minimal level of human intervention. Ontologies have been used as a semantic knowledge source to generate questions automatically. However, most questions that have previously been generated from the use of ontologies have been criticised for their simplicity and lack of cognitive engagement. Furthermore, many existing question generation frameworks primarily focus on the technical aspects, but they lack a strong theoretical foundation. This highlights the need to enrich existing question types by generating more complex questions that cover both a broader and deeper understanding of the associated knowledge and that require more complex reasoning skills than that which is necessary for recalling simple facts. In this thesis, we present a novel ontology-based question generation approach designed to facilitate the creation of complex educational questions, which require larger knowledge coverage and higher cognitive processes. Our method leverages the concept of Query Graphs, a graph-like structure capable of representing natural language queries through appropriate mappings. We propose the use of Query Graphs as a formalism for representing templates that incorporate multiple ontology-based constraints to elevate the level of reasoning required to answer the questions. We demonstrate that this approach is indeed effective and aligns with the assessment of educational experts. To further support the plausibility of our computational framework, we shed light on its consistency with theories from education and cognitive psychology. This provides a solid theoretical foundation that ensures that questions are generated according to principled methods that are grounded in the theory of learning and cognition. The proposed approach is agnostic to the choice of different subject areas or knowledge domains, and independent of the question format. Therefore, the approach proposed is highly general and applicable to a variety of contexts. Being the primary source of knowledge, ontologies have an impact on the effectiveness of the quality of the questions generated. Therefore, we examine how different ontologies perform when applied to the same question generating task. An expert-based study was conducted, leading to the identification of ontology evaluation metrics designed to assess the suitability of domain ontologies for successful use in AQG. These metrics facilitate the reuse of existing ontologies and reduces the need to develop new ones from scratch, thereby lowering the cost of implementing AQG models.14 0Item Restricted Retrieval and Labeling of Documents Using Ontologies: Aided by a Collaborative Filtering(2023) Alshammari, Asma Abdulkarim; Bhatnagar, RajInformation retrieval is one of the common tasks in today’s world and retrieval systems are aided by various text mining and analysis methods. The objective of retrieval is to obtain information resources from a collection that are relevant to a specified query. The retrieval process begins with a query provided by a user. A search engine is then started to find the relevant resources. Typically, the queries are formed using the same terms (words) that also occur within the resources. The situations of a document matching the non-occurring terms are illustrated by the following examples: we want to retrieve documents relevant to some query terms that do not explicitly occur in the documents but are relevant to their contents. We want to retrieve documents using queries that contain labels from the ontology tree, and these labels may not explicitly occur in documents. We may have a large collection of documents in an organization, and various user communities that may want to refer to the documents using their community-specific ontologies. Several information retrieval methods use clustering of documents followed by determining signatures for each cluster describing the terms predominantly present in each of the clusters. We have designed and implemented a clustering algorithm that partitions the data space in a step-wise manner and seeks to optimize clusters that have good-quality signatures representing the documents in the clusters. The clustering algorithm is modeled on a bi-clustering strategy using the spectral co-clustering method at each step and then optimizing towards clusters that have strong representative signatures. We have shown that this clustering algorithm performs better than other known clustering algorithms such as K-Means and Latent Dirichlet Allocation (LDA). We have accomplished our goal of improving information retrieval systems’ capabilities and performance by presenting a new method to generate predicted terms for the documents by using Singular Value Decomposition (SVD) based collaborative filtering methods. We have shown that retrievals made using such recommended terms for documents retrieve correct documents with reasonably high accuracy. In addition, including predicted terms in the clustering process improves the purity of clusters and the quality of retrieval. We have achieved our goal of integrating ontological labels with information retrieval by adding terms to a document from ontologies and using a collaborative filtering approach to associate ontology labels with other relevant documents. We have tested the performance of our method with many cases of integrating ontologies: single ontology label, single large ontology with all complexities of an ontology tree, and multiple ontology trees. We have tested this method on our document collections and have obtained promising results. Our method has higher performance than other existing methods.50 0