## 1 Introduction
In real-world scenarios, ontologies would be ranked and selected not just according to their underlying characteristics, but a lot of importance would be given to how well the ontologies align with the needs and requirements of the organization and its stakeholders. However, the current MCDM solutions for ranking ontologies to aid their selection [14; 15; 16; 17] focus on ranking ontologies based on their underlying characteristics and attributes. Little work has been completed that performs ontology ranking with MCDM methods from the perspectives of the actual users and knowledge engineers. 3. The current MCDM solutions for ranking ontologies [14; 15; 16; 17] to aid their selection rely on quantitative metrics represented with numerical values to evaluate and rank the ontologies; this renders the task of decision-makers and subject experts very difficult, as they are required to handle a large number of numerical values to express their evaluation of alternatives in the process of ranking ontologies. It would be valuable to allow decision-makers and subject experts to use natural language and linguistic terminologies to express their views in the ranking process of ontologies. At present, very little work has been completed pertaining to the usage of linguistic modeling in MCDM solutions for ranking and selection of ontologies. To address the above-mentioned shortcomings of existing MCDM solutions for ontology ranking, this study proposes an enhanced ELECTRE II model based on the concept of the Z-Probabilistic Linguistic Term Set (ZPLTS), which is a combination of Z-numbers [18] and the Probabilistic Linguistic Term Set (PLTS) [19]. The ELECTRE II method is part of a family of outranking MCDM methods that were developed to rank alternatives from best to worst. It is one of the prominent models that is widely used in decision-makingproblems [20; 21; 22; 23; 24; 25]. The ELECTRE methods were developed over five decades ago, and accordingly, they are a mature and well-studied MCDM tool, but nonetheless, the research and development on the methods is still very active up to today [25]. The ELECTRE family is also especially unique being that it has the ability to adapt to various problematic situations: that is, for selection problems, the ELECTRE I may be applied, for ranking problems, the ELECTRE II, III, and IV may be applied, and for classification problems, the ELECTRE Tri may be applied. The concept of PLTS allows decision-makers to provide their evaluations for alternatives with the use of linguistic terms as opposed to numerical values, making it easier to evaluate alternatives. Z-numbers allow a decision-maker to provide an indication of the credibility of their evaluation. The combination of these with the ELECTRE II method enables a powerful model for the task of ontology ranking for selection from a group perspective, using not only quantitative values but also linguistic evaluations from decision-makers. The contributions of the study are threefold: (1) the ability of modeling decision making in ELECTRE with both numerical and linguistic terms, (2) the use of ZPLTS to permit group decision making in ELECTRE and to enable the measurement of the credibility of the different decision-makers' evaluations, (3) a comparative analysis of the proposed enhanced ELECTRE method with existing fuzzy ELECTRE methods on the task of ontology ranking in the mental health domain. The rest of the paper is structured as follows. Section 2 reviews and discusses related literature. ELECTRE II is presented, and the various concepts relevant to the specification of the proposed ZPLTS-ELECTRE II method are defined in Section 3. In Section 4, the detailed specification of the proposed ZPLTS-ELECTRE II method is presented. The experimental results of the application of the ZPLTS-ELECTRE II method to rank a set of ontologies are described in Section 5, and the paper is concluded in Section 6.