## 6 Conclusions and Future Work  
In this paper, the ELECTRE II MCDM method was combined with the concept of ZPLTS to develop a more expressive and real-world modeling technique for decision modeling and decision making, namely, ZPLTS-ELECTRE II. The method was applied to rank nine ontologies comprising knowledge pertaining to mental health and well-being. Ten attributes were used, five of which were quantitative complexity metrics, and the other five were qualitative attributes obtained from four decision-makers' perspectives. The proposed ZPLTS-ELECTRE II method successfully ranked all ontologies from best to worst. In real-world ontology selection problems, it would be more appropriate to model both quantitative and qualitative attributes. Therefore, the ZPLTS-ELECTRE II method is particularly useful in eliciting ontology ranking and selection as a group decision-making problem. The new ZPLTS-ELECTRE II method provides knowledge engineers and ontologists an opportunity to evaluate and select ontologies from a multi-dimensionalperspective as opposed to a singular perspective. The performance of the ZPLTS-ELECTRE II method was compared against that of the traditional ELECTRE II and the PLTS ELECTRE II methods on the same dataset; thereafter, the ZPLTS-ELECTRE II method was compared against previous studies that have used MCDM methods in ontology ranking as well as the existing fuzzy ELECTRE II methods implemented in related studies and displayed superior modeling capabilities to the existing methods.  
The ZPLTS-ELECTRE II method can be used for the modeling of real-world decision-making problems, and it is not confined to ontology ranking. Some examples of real-world scenarios that would greatly benefit from the application of ZPLTS-ELECTRE II include (1) medical diagnosis by multiple doctors by modeling their symptoms qualitatively and quantitatively, (2) identifying and selecting the best site locations for implementing construction projects according to various stakeholders such as engineers, architects, developers, surveyors, etc., and (3) evaluating and selecting the appropriate candidates for job positions based on multiple evaluators, such as managers, staff members, team leaders, etc.  
In the future, more operations for the ZPLTS can be developed in order to further extend the modeling capabilities of the ZPLTS-ELECTRE II method. Another future direction of research would be to apply the ZPLTS-ELECTRE II model for ontology ranking based on different criteria as well as to perform the ranking of ontologies from other domains apart from the mental health domain. The current study focuses on the PLTS, but in many cases, the decision-maker may encounter further uncertainty when providing their evaluation. Therefore, future studies may extend the current ZPLTS-ELECTRE II method to perform under further uncertain environments such as the interval-valued and uncertain probabilistic linguistic term set. Lastly, it would be interesting to apply the ZPLTS-ELECTRE II method to other fields to aid in the task of ranking and selection.  
Conceptualization, A.S. and J.V.F.-D.; methodology, A.S. and J.V.F.-D.; software, A.S.; validation, A.S. and J.V.F.-D.; formal analysis, A.S. and J.V.F.-D.; investigation, A.S. and J.V.F.-D.; resources, A.S. and J.V.F.-D.; data curation, A.S. and J.V.F.-D.; writing--original draft preparation, A.S.; writing--review and editing, A.S. and J.V.F.-D.; visualization, A.S.; supervision, J.V.F.-D.; project administration, A.S. and J.V.F.-D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The dataset of ontologies used in this study can be accessed and downloaded via the BioPortal Ontology Repository [7]. The authors would like to express their appreciation to the reviewers for their valuable efforts. The authors declare no conflict of interest.