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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".