cs.AIOct 27, 2026

GraphRAGProv: GraphRAG Meets How-Provenance

Authors: Wadie El Amrani, Luis Galárraga

Organizations: Independent Toulouse, France · INRIA Rennes, France

Abstract

GraphRAG systems often use knowledge graphs to retrieve relevant context for an LLM that produces the final answer. That makes answers more inscrutable and therefore less appealing for domains where transparency is paramount. We present GraphRAGProv, an architecture that executes SPARQL queries over source-grounded RDF graphs to compute answers and their how-provenance polynomials. These polynomials record which statement occurrences were used and how they were combined. Because each occurrence is linked to its source text, users can follow these links to check whether the extracted statements reflect the text and whether the query combines them to answer the question. Querying thereby becomes a way to inspect and evaluate the text-to-graph transformation. Our interactive demo examines 400 cases from two multi-hop QA benchmarks, revealing extraction and query errors even when the final answer is correct.

Figures & tables

Explore similar work

CardsList
  1. Why Neighborhoods Matter: Traversal Context and Provenance in Agentic GraphRAG

    May 14, 2026Riccardo Terrenzi, Maximilian von Zastrow, Serkan AyvazRAG Citation FaithfulnessData Provenance

  2. LivingRAG: Augmenting Graph RAG with Experience

    Aug 26, 2026Yuzhuo Cui, Zongye Zhang, Qingjie LiuRetrieval-Augmented GenerationGraph Retrieval-Augmented Generation

  3. TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoning

    Oct 7, 2026Ruochi Li, Jianzhe Lin, Haoxuan Zhang +4Graph Retrieval-Augmented GenerationMultimodal RAG