Domain-Specific Knowledge Graphs in RAG-Enhanced Healthcare LLMs
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Abstract (arXiv)
Large Language Models (LLMs) generate fluent answers but can struggle with trustworthy, domain-specific reasoning. We evaluate whether domain knowledge graphs (KGs) improve Retrieval-Augmented Generation (RAG) for healthcare by constructing three PubMed-derived graphs: mathbbG1 (T2DM), mathbbG2 (Alzheimer's disease), and mathbbG3 (AD+T2DM). We design two probes: Probe 1 targets merged AD T2DM knowledge, while Probe 2 targets the intersection of mathbbG1 and mathbbG2. Seven instruction-tuned LLMs are tested across retrieval sources No-RAG, mathbbG1, mathbbG2, mathbbG1 + mathbbG2, mathbbG3, mathbbG1+mathbbG2 + mathbbG3 and three decoding temperatures. Results show that scope alignment between probe and KG is decisive: precise, scope-matched retrieval (notably mathbbG2) yields the most consistent gains, whereas indiscriminate graph unions often introduce distractors that reduce accuracy. Larger models frequently match or exceed KG-RAG with a No-RAG baseline on Probe 1, indicating strong parametric priors, whereas smaller/mid-sized models benefit more from well-scoped retrieval. Temperature plays a secondary role; higher values rarely help. We conclude that precision-first, scope-matched KG-RAG is preferable to breadth-first unions, and we outline practical guidelines for graph selection, model sizing, and retrieval/reranking. Code and Data available here - https://github.com/sydneyanuyah/RAGComparison
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References · 6
- 2023Biomedical knowledge graph-optimized prompt generation for large language modelscited by 31
- 2025Knowledge Graph-extended Retrieval Augmented Generation for Question Answeringcited by 14
- 2025Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modellingcited by 9
- 2025FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphscited by 8
- 2025Trustworthy Medical Question Answering: An Evaluation-Centric Surveycited by 7
- 2025MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generationcited by 3
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- 2023HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses
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