A Comprehensive Review of Data Retrieval and Evaluation Methods for Current Universities Ontological Model

The advent of the big data era is a direct consequence of the rapid and exponential increase in the volume of data generated daily. Many organisations, especially those in industries and academic institutions, are actively involved in the systematic evaluation and investigation of big data analysis. A learning management system (LMS) is a software application or web-based technology utilised to create, execute, and evaluate a particular learning procedure. Universities utilise this LMS system to effectively oversee student data, simplify documentation procedures, monitor student information, provide reports, automate duties, and facilitate the delivery of educational courses, training programmes, learning materials, and development initiatives. By analysing and evaluating this LMS data, new information can be obtained. Graph databases and resource description framework (RDF) ontologies are essential tools for analysing data in an heterogenous large-scale data environment. However, there is a shortage of information regarding the most optimal ontologies that are available and can be implemented to seamlessly combine data from numerous universities e-learning platforms. Hence, this study aims to evaluate the properties of recent higher education institutions and universities ontologies and their usage, analyse the data retrieval methods utilised, and examine the assessment methods employed in them.