ONTOLOGICAL KNOWLEDGE MODELING IN THE DIGITAL TRANSFORMATION OF ACADEMIC INSTITUTIONS

The article examines ontological knowledge modeling tools in the digital transformation of academic institutions. Ontologies formalize knowledge, enable interoperability (e.g., LMS, ERP), and support personalized education. Recent research highlights their use in administrative workflows, educational content structuring, and learning analytics. Case studies (selection of candidates for vacant positions of academic staff, competency-based courses) demonstrate ontologies’ efficacy in automating queries, semantic search, and visualization. Gruber’s portable ontologies, Guarino’s formal classifications, and Sowa’s conceptual graphs are compared, emphasizing their roles in semantic web and knowledge engineering. Uschold–Grüninger’s methodologies are evaluated for educational projects. A hybrid approach (Guarino’s rigor + Uschold– Grüninger’s pragmatism) is proposed for scalable, context-aware ecosystems. Tools like Protégé, WebProtégé, and VocBench are evaluated for collaborative development, terminology, and visualization. Protégé suits OWL design; WebProtégé enables real-time collaboration; VocBench supports SKOS terminology. FAIR-aligned ontologies enhance data reuse in academic repositories. Ontologies also address challenges in data accuracy and redundancy by standardizing metadata, ensuring consistency across platforms. For instance, ontologydriven semantic search reduces ambiguity in academic repositories, improving resource discoverability. Key findings: ontologies integrate fragmented systems (LMS, CRM), improve knowledge management, and enable predictive analytics. Challenges include scaling for big data, cross-domain compatibility, and simplifying development for non-experts. Recommendations: adopt tailored frameworks, prioritize interdisciplinary collaboration, and invest in semantic technologies. Universities should initiate pilot projects, train staff in ontology design, and partner with tech providers to implement modular ontologies. Future research could explore AI-driven ontology generation and adaptive ontologies for real-time data streams.