Аңдатпа
Reproducibility and comparability remain important methodological concerns in domain-specific question answering, particularly in low-resource educational settings. This study examines the impact of ontology-guided context filtering on extractive question answering in the Kazakh language under controlled and reproducible conditions. A benchmark corpus on the History of Kazakhstan was constructed from 11 school textbooks for grades 5–10 and canonically indexed using deterministic section identifiers. Fixed train–test splits, standardized chunking, and unified preprocessing and evaluation scripts ensured consistency across experiments. Three question answering systems were evaluated under identical constraints: a neural extractive baseline operating on unfiltered context, an ontology-only system relying on structured knowledge representations, and a hybrid system applying ontology-guided context filtering prior to neural answer extraction. Filtering was conducted under an inference setting that excludes access to gold document or section identifiers, preventing the use of privileged metadata. The hybrid system demonstrates modest improvements over the neural-only baseline. Exact Match accuracy increases from 68.87% to 70.45%, while the token-level F1 score improves from 80.02% to 81.68%. Ranking-based retrieval metrics also show small gains, with Mean Reciprocal Rank increasing from 0.870 to 0.892 and Recall@10 from 95.1% to 96.9%. Inference latency is reduced from 20.01 ms to 6.83 ms per question. Paired statistical testing confirms that the improvement in F1 is statistically significant, whereas improvements in Exact Match are less conclusive. Ablation experiments suggest that these differences are not attributable to context length reduction alone, but are associated with the interaction between ontology-guided context selection and neural reader training. Overall, ontology-guided filtering can provide incremental benefits for extractive question answering in a domain-specific, low-resource educational setting under transparent and reproducible experimental conditions.