FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis
Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) when providing responses to domain-specific questions. Graph-based RAG approaches, such as GraphRAG, enhance retrieval by capturing semantic relationships within knowledge graphs (KGs). While the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) are becoming prevalent for scientific data management, especially in complex domains such as medicine, existing RAG approaches lack a structured FAIRification of the underlying knowledge resources. This lack limits their potential for FAIR information retrieval in these domains. To address this gap, we introduce FAIR GraphRA...
| Authors | Marlena Flüh, Soo-Yon Kim, Carolin Victoria Schneider et al. |
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| Primary category | cs.IR |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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