## 1 Introduction  
The concept of ontology is becoming increasingly valuable in the field of artificial intelligence (AI) and big data [1]. An ontology is defined as an explicit specification of a shared conceptualization [2], and it is one of the core components facilitating knowledge representation and reasoning in AI. Ontologies describe different domains of discourse, and they play an important role in representing and expressing complex knowledge in a form that facilitates reasoning, computation, and dissemination. Ontologies provide an efficient solution to manage and advance the pressing issues of knowledge and information overload. Ontologies also provide an efficient and effective model for the comprehension of the myriad semantics associated with various knowledge and information, among different systems, agents, and technologies. The field of ontology and ontological engineering has been studied extensively in recent years, and as such, scientists and subject matter experts have developed a wide range of ontologies belonging to various domains. Ontologies are also witnessing a surge in real-world applications; some of their applications are found in areas such as agriculture [3],finance [4], education [5], and engineering [6]. There are currently a wide range of ontologies freely available online for download, hosted in repositories such as the BioPortal [7] and the AgroPortal [8] ontology repositories. However, ontologies are inherently complex structures, which makes them highly arduous to architect and develop. A wide range of expertise is required for the research and development of an ontology. Accordingly, this process can be very costly and time consuming. Whilst some projects may require a new ontology to be developed _de novo_, for most projects, there is already a variety of existing ontologies available to choose from. Therefore, it is more efficient to reuse existing ontologies, possibly with some modifications, as opposed to developing new ontologies _de novo_. Unfortunately, the large number of ontologies available online also has a downside, giving rise to a new problem altogether--the problem of ontology selection. The ontology selection problem arises due to the complexity associated with ontologies, which makes it challenging for users to comprehend and analyze existing ontologies. This problem is worsened when given the choice of multiple ontologies to choose from. It is therefore evident that there is an urgent need for the development of techniques and methods for evaluating and selecting relevant ontologies for the purpose of reuse. There have been attempts by authors [9; 10; 11; 12; 13] to rank ontologies to aid their selection, but only in recent years has there been attempts to rank ontologies from a decision-making perspective [14; 15; 16; 17]. The issue of ontology selection is essentially a multi-criteria decision-making (MCDM) problem, as there are multiple ontologies to choose from, whilst considering multiple characteristics and attributes. Authors have applied MCDM techniques for the task of ontology ranking and selection, such as ELECTRE, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [17], Weighted Linear Combination Ranking Technique (WLCRT) [16], and so on. However, these previous research studies present some shortcomings, which are elaborated on as follows. 1. The existing MCDM solutions for ranking ontologies [14; 15; 16; 17] to aid their selection focus on ranking ontologies from a singular perspective. However, in real-world application, the ranking and selection of suitable ontologies for reuse would involve more than a single person. Rather, a group of experts and stakeholders would collectively evaluate and decide on the suitable ontologies to reuse. At present, very little emphasis has been placed on ontology ranking and selection in a group setting in the existing literature. 2.