Enhanced Biomedical Factoid Question Answering through Biomedical Knowledge Integration

<p>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.</p> <p>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.</p>