## 2 Literature Review  
As indicated earlier, there have been many studies focusing on ontology ranking [9; 10; 11; 12; 13] to aid ontology selection. In a study by [PERSON] and [PERSON] [9], the authors investigated the application of ontology graph analysis measures such as the Centrality and Class Match Measures to rank ontologies, yielding the AKTiveRank system. However, the study gave rise to further questions, and therefore, the authors undertook further study [10] to modify AKTiveRank with the use of structural metrics of ontologies. Another study performed by [PERSON] et al. [11] gave rise to the ARRO system for ontology ranking. ARRO had a substantial amount of similarities with the AKTiveRank system [10], as both systems perform the ranking of ontologies based on their relevance to the users' query terms. In a study by [PERSON] et al. [12], an algorithm was developed to rank ontologies based on a measurement from information theory, namely, Entropy-Based Distribution. In [13], the authors proposed an algorithm, named Onto-DSB, for ontology ranking based on the internal structure of ontologies. The algorithm was applied to an ontology set from Swoogle, and it outperformed the Swoogle and AKTiveRank techniques. The aforementioned studies do not use decision making in the ranking of ontologies. In recent years, there has been an interest in the application of MCDM methods for the task of ontology ranking. One of the first studies was by [PERSON] et al. [14], where the authors implemented the ELECTRE I and ELECTRE III methods to rank ontologies. A similar study [15] applied the ELECTRE I to rank a set of biomedical ontologies. [PERSON] and [PERSON] [16] proposed a framework known as C-Rank, which uses a MCDM method called the Weighted Linear Combination Ranking technique (WLCRT) to rank ontologies. Another study by [PERSON] [17] applied three MCDM methods, namely, the Weighted Sum Model (WSM), the Weighted Product Model (WPM), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to rank ontologies. The studies [15; 16; 17] ranked ontologies based on their complexity metrics. The aforementioned MCDM solutions for ranking ontologies to aid their selection perform the ranking based on the underlying structure and characteristics of the ontologies. However, in real-world applications, it would be important to rank ontologies for selection according to how well they align with the needs and requirements of the users and stakeholders for a project. In this regard, there have been some studies with an emphasis on ranking ontologies according to their applicability to the users' needs. One prominent study was performed by [PERSON] and [PERSON] [26], where the authors proposed the OntoMetric model that allows users to evaluate the suitability of ontologies in light of the requirements of their projects. The study made use of 160 characteristics to compare ontologies, which yielded a score that was used to rank the ontologies. However, the system had the drawback of it being time consuming to perform such a large number of evaluations, and it also has the potential to be extremely biased depending on the decision-maker [27]. To overcome this issue, a recent study by [PERSON] et al. [27] proposed an Ontology Usability Scale. The authors extracted 10 items from the 160 characteristics in [26] that were related to the usability of ontologies, which they then used to form the Ontology Usability Scale. The scale allows a user to provide their evaluation for the 10 items in light of each ontology with the use of a Likert scale. The qualitative attributes used in this study were adopted from the study in [27]. Despite the use of MCDM modeling in ontology ranking [14; 15; 16; 17], only quantitative metrics have been used as evaluation criteria. There have been no attempts to combine MCDM modeling with usability metrics for ontology ranking and selection.