For querying a clinical papers knowledge base, KGRAG (Knowledge Graph RAG) typically delivers better results than vector-based RAG, particularly when dealing with complex relationships, provenance requirements, and multi-step reasoning[1][2][3][4]. KGRAG leverages structured representations - mapping entities (such as drugs, diseases, and genes) and their connections—making it especially valuable in clinical domains where context, relationships, and explainability matter[5][2]. Vector RAG excels in fast semantic searches and retrieving contextually similar documents, which is useful for straightforward queries. However, it can struggle with nuanced clinical searches requiring reasoning over multiple linked papers or concepts, such as understanding drug interactions, the interplay of comorbidities, or tracing annotated clinical guidelines across sources[5][6]. In contrast, KGRAG can traverse relationships, synthesize evidence from multiple sources, and provide structured, interpretable, and provenance-backed responses, making it suitable for clinical and biomedical Q&A where correctness and context are critical[1][2][4]. Recent benchmarks show KGRAG frameworks improve accuracy, reduce token consumption, and enable more reliable retrieval of information rooted in established, curated biomedical knowledge graphs—outperforming vanilla RAG methods on domain-specific literature queries and multiple-choice question datasets for clinical medicine[2][4]. In summary, for a clinical papers knowledge base: - **KGRAG is preferred** for explainable answers, complex reasoning, and evidence synthesis across structured data[2]. - **Vector RAG is useful** for rapid, semantic similarity-based document retrieval, but less effective in clinical settings needing structured, context-rich insights[5][6]. #date/2025/10/03 Sources [1] Practical Guide to Supercharge RAG with Knowledge Graphs https://learnopencv.Com/graphrag-explained-knowledge-graphs-medical/ [2] Biomedical knowledge graph-optimized prompt generation for ... Https://academic.Oup.Com/bioinformatics/article/40/9/btae560/7759620 [3] Domain-Specific Retrieval-Augmented Generation Using ... Https://arxiv.Org/html/2410.02721v1 [4] Medical Graph RAG: Evidence-based ... Https://aclanthology.Org/2025.Acl-long.1381.Pdf [5] Graph Rag Vs Vector RAG: Complete guide for Beginners https://www.Chitika.Com/graph-rag-vs-vector-rag/ [6] Biomedical Q&A Using RAG https://www.Emergentmind.Com/articles/2509.05505 [7] Vector Databases vs. Knowledge Graphs for RAG - Paragon https://www.Useparagon.Com/blog/vector-database-vs-knowledge-graphs-for-rag [8] Knowledge Graph vs. Vector RAG: Optimization & Analysis https://neo4j.Com/blog/developer/knowledge-graph-vs-vector-rag/ [9] RAG vs. GraphRAG: A Systematic Evaluation and Key ... Https://arxiv.Org/html/2502.11371v1 [10] Performance of Retrieval-Augmented Generation (RAG) on ... Https://intuitionlabs.Ai/articles/rag-performance-pharmaceutical-documents [11] Knowledge graph vs vector database: Which one to choose? Https://www.Falkordb.Com/blog/knowledge-graph-vs-vector-database/ [12] KGRAG-Ex: Explainable Retrieval-Augmented Generation ... Https://arxiv.Org/html/2507.08443v1 [13] Vector-RAG vs Graph RAG: Key Differences Explained and ... Https://www.Designveloper.Com/blog/vector-rag-vs-graph-rag/ [14] Large Language Model–Driven Knowledge Graph ... Https://pmc.Ncbi.Nlm.Nih.Gov/articles/PMC11986385/ [15] RAG-BioQA Retrieval-Augmented Generation for Long- ... Https://arxiv.Org/abs/2510.01612 [16] KGRAG-Ex: Explainable Retrieval-Augmented Generation ... Https://arxiv.Org/pdf/2507.08443.Pdf [17] BiomedRAG: A retrieval augmented large language model ... Https://www.Sciencedirect.Com/science/article/pii/S1532046424001874 [18] Biomedical Literature Q&A System Using Retrieval ... Https://arxiv.Org/html/2509.05505v1 [19] A retrieval-augmented knowledge mining method with ... Https://pmc.Ncbi.Nlm.Nih.Gov/articles/PMC12448786/ [20] Improving Retrieval-Augmented Generation in Medicine ... Https://pmc.Ncbi.Nlm.Nih.Gov/articles/PMC11997844/