Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908491510185984 |
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| author | Nazi, Zabir Al Hristidis, Vagelis McLean, Aaron Lawson Meem, Jannat Ara Chowdhury, Md Taukir Azam |
| author_facet | Nazi, Zabir Al Hristidis, Vagelis McLean, Aaron Lawson Meem, Jannat Ara Chowdhury, Md Taukir Azam |
| contents | Effective Question Answering (QA) on large biomedical document collections requires effective document retrieval techniques. The latter remains a challenging task due to the domain-specific vocabulary and semantic ambiguity in user queries. We propose BMQExpander, a novel ontology-aware query expansion pipeline that combines medical knowledge - definitions and relationships - from the UMLS Metathesaurus with the generative capabilities of large language models (LLMs) to enhance retrieval effectiveness. We implemented several state-of-the-art baselines, including sparse and dense retrievers, query expansion methods, and biomedical-specific solutions. We show that BMQExpander has superior retrieval performance on three popular biomedical Information Retrieval (IR) benchmarks: NFCorpus, TREC-COVID, and SciFact - with improvements of up to 22.1% in NDCG@10 over sparse baselines and up to 6.5% over the strongest baseline. Further, BMQExpander generalizes robustly under query perturbation settings, in contrast to supervised baselines, achieving up to 15.7% improvement over the strongest baseline. As a side contribution, we publish our paraphrased benchmarks. Finally, our qualitative analysis shows that BMQExpander has fewer hallucinations compared to other LLM-based query expansion baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models Nazi, Zabir Al Hristidis, Vagelis McLean, Aaron Lawson Meem, Jannat Ara Chowdhury, Md Taukir Azam Information Retrieval Machine Learning Effective Question Answering (QA) on large biomedical document collections requires effective document retrieval techniques. The latter remains a challenging task due to the domain-specific vocabulary and semantic ambiguity in user queries. We propose BMQExpander, a novel ontology-aware query expansion pipeline that combines medical knowledge - definitions and relationships - from the UMLS Metathesaurus with the generative capabilities of large language models (LLMs) to enhance retrieval effectiveness. We implemented several state-of-the-art baselines, including sparse and dense retrievers, query expansion methods, and biomedical-specific solutions. We show that BMQExpander has superior retrieval performance on three popular biomedical Information Retrieval (IR) benchmarks: NFCorpus, TREC-COVID, and SciFact - with improvements of up to 22.1% in NDCG@10 over sparse baselines and up to 6.5% over the strongest baseline. Further, BMQExpander generalizes robustly under query perturbation settings, in contrast to supervised baselines, achieving up to 15.7% improvement over the strongest baseline. As a side contribution, we publish our paraphrased benchmarks. Finally, our qualitative analysis shows that BMQExpander has fewer hallucinations compared to other LLM-based query expansion baselines. |
| title | Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2508.11784 |