Methods and Tools for Refactoring Ontologies
DOI:
https://doi.org/10.15802/stp2026/356061Keywords:
information technology, ontology, refactoring, knowledge bases, refactoring methods, quality metrics, software, schema transformation, refactoring qualityAbstract
Purpose. This work is aimed at investigating the evolution of the refactoring concept – from a tool for optimizing program code to a powerful means of improving data structures, algorithms, and business processes. The main purpose of the study is to examine the historical development of software and ontology refactoring, as well as the possibilities of applying improvement methods to ontologies as a specific type of software structure that plays a key role in knowledge-oriented systems, the Semantic Web, and next-generation cyber-physical systems. Methodology. The methodological basis of the study consists of general scientific and specialized methods of analysis and synthesis. A systematic review of scientific publications in the fields of software refactoring and ontology engineering was conducted using full-text and abstract scientometric databases. Comparative analysis methods were applied to compare different refactoring approaches, structural and functional analysis was used to study changes in software and ontology models, and classification methods were employed to group existing refactoring techniques by level of abstraction and application domain. Finding. Within the scope of the study, a comprehensive analysis of scientific publications devoted to refactoring and ontology engineering was performed using full-text and abstract databases. The evolution of refactoring approaches was examined, including data schema transformations, conceptual refactoring, modification of integrity constraints, and the development of tools for assessing the quality of changes. Particular attention was paid to the application of refactoring to ontologies, including an analysis of its impact on modularity, coherence, and knowledge reuse. Originality. The scientific novelty of the work lies in the systematic study of existing refactoring methods and their classification by type. A comprehensive analysis of the strengths and weaknesses of each approach was carried out, enabling a well-founded selection of optimal strategies for software improvement. The proposed approach contributes to a deeper understanding of refactoring mechanisms in the context of various application domains. Practical value. The presented results can be used in the design, maintenance, and evolution of knowledge-oriented systems, where structural consistency and semantic support play an important role. Prospects for further research are outlined, including the automation of ontology refactoring processes using machine learning methods, the expansion of evaluation metric systems, and the adaptation of the proposed solutions to application domains characterized by a high degree of change dynamics.
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