In recent years, educational institutions worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most 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 an open 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 his profile, including research records. Further, the resulted data is available to reuse in the future for different purposes in 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.
Ashour, G., Al-Dubai, A., Romdhani, I., & Alghazzawi, D. (2022). Ontology-Based Linked Data to Support Decision Making within Universities. Mathematics, 10(17), Article 3148. https://doi.org/10.3390/math10173148