Ontology-Based Linked Data to Support Decision-Making within Universities

Ghadeer Ashour (), Ahmed Al-Dubai, Imed Romdhani and Daniyal Alghazzawi
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Ghadeer Ashour: School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Ahmed Al-Dubai: School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Imed Romdhani: School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Daniyal Alghazzawi: Department of Information Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2022, vol. 10, issue 17, 1-21

Abstract: 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.

Keywords: decision support systems; educational ontology; federated query; intelligent systems; linked data; semantic data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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