Enhanced Biomedical Factoid Question Answering through Biomedical Knowledge Integration
Bibliographic record
Abstract
Factoid question answering requires short, factual responses. In the biomedical domain, this involves extracting specific answers from articles. Despite recent progress using pre-trained language models (LMs) for passage retrieval and text comprehension, challenges persist due to limited biomedical datasets, affecting LM accuracy. This thesis introduces the Biomedical Knowledge-enhanced Question Answering Framework (BK-QAF), incorporating knowledge from the Unified Medical Language System (UMLS) to enhance comprehension and reasoning. The framework uses a graph attention network (GAT) to prioritize UMLS concepts by relevance, facilitating precise answers. BK-QAF’s effectiveness was assessed using three distinct GAT architectures, demonstrating robustness across different configurations. Empirical evaluations with the BioASQ datasets show that BK-QAF significantly outperforms state-of-the-art baselines in Strict Accuracy (SAcc) and Mean Reciprocal Rank (MRR). This thesis highlights BK-QAF’s potential to improve performance in biomedical question answering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".