Ontology-Driven Text Classification and Data Mining: Beyond Keywords Toward Semantic Intelligence
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".