No abstract

Educational institutions suffer from the enormous amount of data that keeps growing continuously. These data are usually scattered and unorganised, and it comes from different resources with different formats. Besides, modernization vision within these institutions aims to reduce human action and replace it with automatic devices interactions. To have the full benefit from these data and use it within the modern systems, they have to be readable and understandable by machines. Those data and knowledge with semantic descriptions make an easy way to monitor and manage decision processes within universities to solve many educational challenges. In this study, an educational ontology is developed to model the semantic courses and academic profiles in universities and use it to solve the challenge of assigning the most appropriate academic teacher to teach a specific course.

In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different purposes in the academic domain. Finally, we compared the results of this link with previous work, as evidence of the accuracy of leveraging this technology to improve decisions within universities.