Citation: Ashour, G.; Al-Dubai, A.;
Romdhani, I.; Alghazzawi, D.
Ontology-Based Linked Data to
Support Decision-Making within
Universities. Mathematics 2022, 10,
3148. https://doi.org/10.3390/
math10173148
Academic Editors: Sławomir
Nowaczyk, Rita P. Ribeiro, Grzegorz
Nalepa and Seifedine Kadry
Received: 29 June 2022
Accepted: 29 August 2022
Published: 2 September 2022
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Article
Ontology-Based Linked Data to Support Decision-Making
within Universities
Ghadeer Ashour
1,2,
* , Ahmed Al-Dubai
1
, Imed Romdhani
1
and Daniyal Alghazzawi
3
1
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
2
Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
3
Department of Information Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
* Correspondence: gaashor@kau.edu.sa
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
MSC: 68-11
1. Introduction
In the last few years, higher education institutions (HEIs), such as universities, are
increasingly using more modern technologies to automate different activities and improve
the quality of their data. One of these technologies is representing academic data seman-
tically in RDF format. Research, employment, and decision-making are examples of the
challenging activities that higher education (HE) entails. Due to the nature of and frequent
increase in academic data, semantic representation succeeds in solving several challenges in
the educational domain. Although semantics have proven effective in many aspects, some
shortages were diagnosed, such as dealing with missing information and the continuous
update of data.
On the other hand, HEIs, such as universities, are increasingly using linked data (LD)
to make public information (academic programs, research outputs, facilities, etc.) available
as linked data on the Web. This trend opens the opportunities to use these data to automate
the accomplishment of main processes within several institutions. Digital libraries are one
of the institutions that use LD to publish scientific data and make them available to be used
freely by others.
This research examines the outcome of a linked data creation cycle in the context of
academic scientific research. It relies on Saudi university quality accreditation regulations.
The study investigates the added value of leveraging the semantic technology of linked data
in decision-making to produce accurate results for different tasks. The conducted scenario
is applied to the local data of the Faculty of Computing and Information Technology
Mathematics 2022, 10, 3148. https://doi.org/10.3390/math10173148 https://www.mdpi.com/journal/mathematics
