FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis

Integrating FAIR Digital Objects into graph-based retrieval to improve accuracy, coverage, and explainability for biomedical question answering

Marlena Flüh; Soo-Yon Kim; Carolin Victoria Schneider; Sandra Geisler· 2026· DOI 10.48550/arXiv.2607.11464

The core problem

Retrieval-Augmented Generation (RAG) mitigates key limitations of Large Language Models (LLMs) when answering domain-specific questions by grounding generation in external knowledge. Graph-based RAG approaches, such as GraphRAG, further enhance retrieval by capturing semantic relationships within knowledge graphs (KGs). In parallel, the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—have become prevalent for scientific data management, particularly in complex domains such as medicine. However, existing RAG approaches lack a structured FAIRification of the underlying knowledge resources, limiting their potential for FAIR information retrieval in these domains. To address this gap, the authors introduce FAIR GraphRAG, a novel framework that integrates FAIR Digital Objects (FDOs) as the fundamental units of a graph-based retrieval system. Each graph node represents an FDO that incorporates core data, metadata, persistent identifiers, and semantic links. The framework was co-designed by physicians and computer scientists to ensure both technical and clinical relevance. The work demonstrates feasibility by applying FAIR GraphRAG to a biomedical dataset in

The framework was applied to a biomedical dataset in gastroenterology, specifically RNA-sequencing data. The evaluation focused on question answering accuracy, coverage, and explainability. Results show that FAIR GraphRAG significantly improves all three metrics compared to baseline RAG approaches, particularly for complex queries involving metadata and ontology links. The integration of FDOs ensures that retrieved information adheres to FAIR principles, enhancing trust and reusability. While exact quantitative metrics are not provided in the abstract, the authors report significant improvements. The system's ability to traverse semantic links enables it to answer queries that require combining data and metadata, which are common in clinical and research settings. The co-design with physicians ensured that the evaluation reflected clinically relevant questions. The explainability improvement stems from the graph structure, which provides a traceable path from query to answer via the retrieved subgraph. This is crucial in medical domains where transparency is essential. The application to RNA-sequencing data demonstrates feasibility for complex biomedical data types, and the authors

Retrieval-Augmented Generation (RAG) mitigates key limitations of Large Language Models (LLMs) when answering domain-specific questions by grounding generation in external knowledge. Graph-based RAG approaches, such as GraphRAG, further enhance retrieval by capturing semantic relationships within knowledge graphs (KGs). In parallel, the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—have become prevalent for scientific data management, particularly in complex domains such as medicine. However, existing RAG approaches lack a structured FAIRification of the underlying knowledge resources, limiting their potential for FAIR information retrieval in these domains. To address this gap, the authors introduce FAIR GraphRAG, a novel framework that integrates FAIR Digital Objects (FDOs) as the fundamental units of a graph-based retrieval system. Each graph node represents an FDO that incorporates core data, metadata, persistent identifiers, and semantic links. The framework was co-designed by physicians and computer scientists to ensure both technical and clinical relevance. The work demonstrates feasibility by applying FAIR GraphRAG to a biomedical dataset in gastroenterology, specifically RNA-sequencing data, and shows improvements in question answering accuracy, coverage, and explainability, especially for complex queries involving metadata and ontology links.

FAIR GraphRAG constructs a graph-based retrieval system where each node is a FAIR Digital Object (FDO). An FDO encapsulates four components: core data, metadata, persistent identifiers (PIDs), and semantic links. The framework leverages LLMs to support two key processes: (1) schema construction for the graph, and (2) automated extraction of content and metadata from data sources. This LLM-driven automation reduces manual curation effort while maintaining adherence to FAIR principles. The graph structure enables retrieval that follows semantic relationships, allowing the system to answer queries that require traversing metadata and ontology links. The co-design process involved physicians and computer scientists, ensuring that the schema and retrieval mechanisms address real clinical needs. Formally, let the knowledge graph be G = (V, E) where each node v \in V represents an FDO and each edge e \in E represents a semantic link between FDOs. For a query q, the retrieval process identifies a relevant subgraph G_{q} \subseteq G by maximizing a relevance score S (q, G_{q}) that combines semantic similarity and graph connectivity. The LLM then generates an answer conditioned on the retrieved subgraph: P (a ∣ q, G_{q}) . The FAIRification ensures that each node's metadata and PID are machine-actionable, supporting findability and reusability. The overall architecture is illustrated below:

The work shows the feasibility of combining FAIR data practices with graph-based retrieval techniques. By making FDOs the fundamental units, FAIR GraphRAG ensures that every piece of retrieved information is findable, accessible, interoperable, and reusable. This is a departure from traditional RAG systems that treat documents as opaque chunks. The use of LLMs for schema construction and metadata extraction automates a traditionally labor-intensive process, though it may introduce biases or errors that require validation. The co-design with physicians highlights the importance of domain expertise in building clinically relevant AI systems. The improvements in accuracy, coverage, and explainability are particularly notable for complex queries, suggesting that the graph structure and FAIRification are key enablers. However, the evaluation is limited to a single biomedical dataset; broader validation across domains is needed. The authors suggest potential applications in education and business, where FAIR principles and semantic relationships are also valuable. Future work could explore scalability, handling of dynamic data, and integration with other FAIR tools. Overall, FAIR GraphRAG represents a promising step toward trustworthy, explainable, and semantically rich retrieval-augmented generation.

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