Ontology-Driven Text Classification and Data Mining: Beyond Keywords Toward Semantic Intelligence

The exponential increase of textual information on digital platforms exposes the shortcomings of conventional classification approaches, which often struggle to interpret meaning beyond surface-level keywords.This research explores the use of ontologies as an innovative approach to enhance semantic understanding in text classification.Ontologies serve as formal frameworks for representing domain knowledge, allowing systems to grasp complex conceptual relationships beyond simple statistical correlations.The paper provides a systematic review of ontology-based classification techniques, detailing their theoretical foundations, integration methods-from vector enrichment to deep learning architecturesand their effectiveness in fields like medicine and multilingual contexts.An empirical validation demonstrates that incorporating ontologies significantly improves classification performance, especially when combined with transformer-based models.Nonetheless, challenges such as scalability, multilingual support, and computational complexity remain.The study concludes with practical recommendations for implementation and suggests future research directions, including dynamic ontology learning, lightweight integration frameworks, and semantic alignment across languages.Ontology-driven classification emerges as a promising pathway toward more intelligent, interpretable, and domainspecific text analysis systems.