[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86238-en":3,"doc-seo-86238-105":29,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86238,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","FAIR GraphRAG Retrieval-Augmented Generation Approach for Semantic Data Analysis","Retrieval-Augmented Generation (RAG) addresses limitations of Large Language Models (LLMs) for domain-specific questions. Graph-based RAG such as GraphRAG improves retrieval by leveraging semantic relationships in knowledge graphs. However, existing approaches do not provide a structured FAIRification of knowledge resources. FAIR GraphRAG integrates FAIR Digital Objects (FDOs) as graph nodes containing core data, metadata, persistent identifiers, and semantic links, using LLMs for schema construction and extraction. Applied to biomedical gastroenterology RNA-seq data, it increases QA accuracy, coverage, and explainability for complex metadata- and ontology-linked queries.","FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis  \nMarlena Fl¨uh   \nData Stream Management and Analysis RWTH Aachen University Aachen, Germany [marlena.flueh@rwth-aachen.de](marlena.flueh@rwth-aachen.de)  \nSoo-Yon Kim   \nData Stream Management and Analysis RWTH Aachen University Aachen, Germany [kim@dbis.rwth-aachen.de](kim@dbis.rwth-aachen.de)  \nCarolin Victoria Schneider   \nGastroenterology, Metabolic Diseases and Intensive Care University Hospital RWTH Aachen Aachen, Germany cschneider@ukaachen.de  \nSandra Geisler   \nData Stream Management and Analysis RWTH Aachen University Aachen, Germany [geisler@dbis.rwth-aachen.de](geisler@dbis.rwth-aachen.de)  \narXiv :2607 . 1 1464v 1 [ cs .IR] 13 Jul 2026  \nAbstract—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 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. We leverage LLMs to support schema construction and automated extraction of content and metadata from data sources. The framework was co-designed by physiciansand computer scientists to ensure technical and clinical relevance. We apply FAIR GraphRAG to a biomedical dataset in gastroenterology, demonstrating its applicability to RNA-sequencing data. Beyond ensuring adherence to the FAIR principles, FAIR GraphRAG significantly improves question answering accuracy, coverage, and explainability, particularly for complex queries involving metadata and ontology links. This work shows the feasibility of combining FAIR data practices with graph-based retrieval techniques. We see potential for applying our approach to other specialized fields such as education and business.  \nIndex Terms—retrieval-augmented generation, FAIR data principles, FAIR Digital Object, knowledge graph construction, large language model  \nI. INTRODUCTION  \nImagine a clinician asking: “Does the patient’s diagnosis require dose adjustment for prescribed medication?” — a query that requires integrating patient records with standardized drug databases. In practice, patient diagnoses often use custom terms, while drug information relies on standardized vocabularies such as SNOMED CT 1. Without linking patient  \n1[https://www.nlm.nih.gov/healthit/snomedct/index.html](https://www.nlm.nih.gov/healthit/snomedct/index.html)  \ndata to standard ontologies, even advanced AI systems fail to match diagnoses to correct dosing recommendations. This mismatch leads to incomplete or incorrect results and can compromise patient care.  \nAddressing such challenges requires sophisticated methods for extracting and connecting knowledge from different data sources. Recent advances in Large Language Models (LLMs) within natural language processing have revolutionized the extraction of information from diverse sources such as text and tabular data [1] . These advancements impact various fields including healthcare, finance and education [2], [3] . However, despite their capabilities, LLMs lack domain-specific knowledge, when it is not part of the LLMs’ training corpus. This knowledge gap can lead to hallucination or factually incorrect outputs [2], [4] .  \nThis is why recent studies have focused on RetrievalAugmented Generation ","cbCaibDKya66G33o","https://ap.wps.com/l/cbCaibDKya66G33o","pdf",644671,4,1,"English","en",105,"# Introduction\n## Retrieval-Augmented Generation and Limitations\n## Knowledge Graphs and GraphRAG\n## FAIRification and the Proposed FAIR GraphRAG Framework","[{\"question\":\"What benefits does FAIR GraphRAG show when applied to biomedical RNA-sequencing data?\",\"answer\":\"Applied to gastroenterology data, it improves question answering accuracy, coverage, and explainability, especially for complex queries involving metadata and ontology links.\"}]",1784209717,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"fair-graphrag-retrieval-augmented-generation-approach-for-semantic-data-analysis","",{"@graph":35,"@context":76},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":20},"https://docshare.wps.com/document/fair-graphrag-retrieval-augmented-generation-approach-for-semantic-data-analysis/86238/",{"url":51,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"What benefits does FAIR GraphRAG show when applied to biomedical RNA-sequencing data?","Question",{"text":74,"@type":75},"Applied to gastroenterology data, it improves question answering accuracy, coverage, and explainability, especially for complex queries involving metadata and ontology links.","Answer","https://schema.org",{"og:url":51,"og:type":78,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":80,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,118,121,125],{"id":21,"doc_module":4,"doc_module_name":45,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":45,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":28,"slug":117},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":28,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":97,"slug":128},19,"General","general"]