## 2 Literature Review
Furthermore, there is little or no work that has been completed in facilitating ontology ranking and selection from the combined perspective of both the quantitative characteristics of ontologies as well as their level of usability for specific projects. The field of MCDM has also seen many improvements and enhancements in recent years, with the combination of other fields such as fuzzy set theory and linguistic terms. The aim of this study is to enhance the ELECTRE II MCDM method with the \(Z\)-Probabilistic Linguistic Term Set to address the above-mentioned problems. The ELECTRE II method is part of the ELECTRE family of MCDM methods, along with ELECTRE I, III, IV, and Tri. ELECTRE I was originally developed by [PERSON] [28] in 1968, and thereafter, the subsequent versions were developed as improvements of ELECTRE I. ELECTRE II [29] was developed to deal with ranking problems. The ELECTRE II model is based on the concepts of concordance and discordance, and it introduced the notion of strong and weak outranking graphs. Since its inception, ELECTRE II has been applied to a variety of domains, such as education [23], finance and investment [20], engineering [21], construction and built environment [22], and agriculture [24]. More applications of ELECTRE II can be found in [25]. These applications of ELECTRE II were based on the traditional ELECTRE II model; however, there have been many enhancements of the method by combining it with fuzzy structures for decision making in fuzzy environments. This enables the ELECTRE II model to deal with uncertain and imprecise data, thereby providing a solution to the problem of obtaining accurate information from decision-makers. Authors have proposed many variations of ELECTRE II with different types of fuzzy sets. [PERSON] et al. [30] developed an ELECTRE II model based on fuzzy set theory to rank alternatives having uncertain information. The authors applied the method to the task of ranking third party logistics providers. In another study by [PERSON] and [PERSON] [31], the intuitionistic ELECTRE II model was proposed and applied to rank inequalities faced by women in society. The intuitionistic fuzzy set was enhanced to overcome its restrictions, leading to the Pythagorean fuzzy set proposed by [PERSON] [32]. [PERSON] et al. [33] developed an ELECTRE II model to perform group decision making under the Pythagorean fuzzy environment, and the authors applied the method to a supplier selection scenario. [PERSON] and [PERSON] [34] combined the hesitant fuzzy set with ELECTRE II to yield the HF-ELECTRE II method. A study [35] integrated the concept of bipolar fuzzy sets with ELECTRE II to formulate the BF-ELECTRE II method. The authors then applied the model to a problem of selecting an optimal business location and choosing the best supplier. Another study by [PERSON] et al. [36] developed an ELECTRE II method to perform under a single-valued neutrosophic environment in a group decision-making setting. Furthermore, due to the fact that in real-world decision problems, it is more appropriate for decision-makers to use words to express their opinions, as opposed to numerical values, authors have attempted to add linguistic modeling capabilities to ELECTRE II. [PERSON] [37] proposed the concept of linguistic variables, allowing decision-makers to express their opinions in a more natural manner. Since decision-makers are often hesitant when expressing their evaluations, the hesitant fuzzy linguistic term set (HFLTS) was proposed by [PERSON] et al. [38]. This allowed decision-makers to provide more than one linguistic term for each evaluation. The concept of HFLTS was then applied to ELECTRE II by [PERSON] et al. [39]. However, the main disadvantage of HFLTS is that it assigns an equal importance weighting to all linguistic terms, which is often not desired in real-world decision-making cases. To overcome this, [PERSON] et al.