Visual-aided ontology-based ranking on multidimensional data: a case study in academia

Abstract

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.