A Linguistic q-Rung Orthopair ELECTRE II Algorithm for Fuzzy Multi-Criteria Ontology Ranking

In recent years, interest in the application of ontologies in various domains of knowledge has grown significantly. Ontologies are widely used in a myriad of areas, such as artificial intelligence, data integration, knowledge management, and the semantic web, to name but a few. However, despite the widespread adoption, there exist a range of problems associated with ontologies, such as the complexity and cognitive challenges associated with ontology engineering, design, and development. One of the solutions to these challenges is to reuse existing ontologies rather than developing new ontologies afresh for new applications. The reuse of ontologies that describe a knowledge domain is a complex task consisting of many aspects. One of the key aspects involves ranking ontologies to aid in their selection. Various techniques have been proposed for this task, but many of them fall short in their expressiveness and ability to capture the cognitive aspects of human-like decision-making processes. Furthermore, much of the existing research focuses on an objective approach to ontology ranking, but it is unquestionable that a wide range of aspects pertaining to the quality of an ontology simply cannot be captured in a quantitative manner. Existing ranking models fail to provide a robust and flexible canvas for facilitating qualitative ontology ranking and selection for reuse. To address the aforementioned shortcomings of existing ontology ranking approaches, this study proposes a novel algorithm for ranking ontologies that extends the Elimination and Choice Translating Reality (ELECTRE) multi-criteria decision-making method with the Linguistic q-Rung Orthopair Fuzzy Set (Lq-ROFS-ELECTRE II), allowing the expression of uncertainty in a more robust and precise manner. The new Lq-ROFS-ELECTRE II algorithm was applied to rank a set of 19 ontologies of the machine learning (ML) domain. The ML ontologies were evaluated using a set of seven qualitative criteria extracted from the Ontometric framework. The proposed Lq-ROFS-ELECTRE II algorithm was then applied to rank the 19 ontologies in light of the seven criteria. The ranking results obtained were compared against the quantitative rankings of the same 19 ontologies using the traditional ELECTRE II algorithm, and confirmed the validity of the ranking performed by the proposed Lq-ROFS-ELECTRE II algorithm and its effectiveness in the task of ontology ranking. Furthermore, a comparative analysis of the proposed Lq-ROFS-ELECTRE II against existing MCDM methods and other existing fuzzy ELECTRE II methods displayed its superior modeling capabilities that allow for more natural decision evaluation from subject experts in real-world applications and allow the decision-maker to have much flexibility in expressing their preferences. These capabilities of the Lq-ROFS-ELECTRE II algorithm make it applicable not only in ontology ranking, but in any domain where there exist decision-making scenarios that comprise multiple conflicting criteria under uncertainty.