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Purpose The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain. Design/methodology/approach The paper presents a method for adapting semantic web technologies on multiple criteria decision-making algorithms to endow to them dynamic characteristics. It also showcases the enhancement of the decision-making process by visual analytics. Findings The semantic enhanced ranking method enables the reproducibility and transparency of ranking results, while the visual representation of this information further benefits decision makers into making well-informed and insightful deductions about the problem. Research limitations/implications This approach is suitable for application domains that are ranked on the basis of multiple criteria. Originality/value The discussed approach provides a dynamic ranking methodology, instead of focusing only on one application field, or one multiple criteria decision-making method. It proposes a framework that allows integration of multidimensional, domain-specific information and produces complex ranking results in both textual and visual form.

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Abstract-Academia is a complex socio-technical system with multiple aspects and constituents that involve various stakeholders. In order to address stakeholders' needs and to assist the institutional accountability, this complexity should be considered during the development of academic services. We have designed a dynamic multidimensional ranking approach, easily modifiable to address user requirements, so as to assess and compare the university performance with a clear view to the support of effective institutional strategic planning and policy making. Our approach comprises the following components: the AcademIS ontology to model the academic domain and its multiple dimensions, the AcademIS Information System to manage and display the academic information, published in Linked Open Data format and the visual-aided Multiple Criteria Decision Making component, to evaluate and rank the performance of the academic units. The data are aggregated from several sources, in different formats, LODified by our system, and presented to the user by the interface to ultimately assist the decision making process.