Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE

• URL: http://arxiv.org/abs/2509.03626v1
• Date: Wed, 03 Sep 2025 18:29:30 GMT
• Title: Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
• Abstract summary: We develop a method-agnostic, perturbation-based framework that provides token and component-level interoperability for Graph RAG using SMILE.<n> KG-SMILE identifies the graph entities and relations most influential to generated outputs, thereby making RAG more transparent.<n>Our findings show that KG-SMILE produces stable, human-aligned explanations, demonstrating its capacity to balance model effectiveness with interpretability.
• Score: 0.03908906925636173
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generative AI, such as Large Language Models (LLMs), has achieved impressive progress but still produces hallucinations and unverifiable claims, limiting reliability in sensitive domains. Retrieval-Augmented Generation (RAG) improves accuracy by grounding outputs in external knowledge, especially in domains like healthcare, where precision is vital. However, RAG remains opaque and essentially a black box, heavily dependent on data quality. We developed a method-agnostic, perturbation-based framework that provides token and component-level interoperability for Graph RAG using SMILE and named it as Knowledge-Graph (KG)-SMILE. By applying controlled perturbations, computing similarities, and training weighted linear surrogates, KG-SMILE identifies the graph entities and relations most influential to generated outputs, thereby making RAG more transparent. We evaluate KG-SMILE using comprehensive attribution metrics, including fidelity, faithfulness, consistency, stability, and accuracy. Our findings show that KG-SMILE produces stable, human-aligned explanations, demonstrating its capacity to balance model effectiveness with interpretability and thereby fostering greater transparency and trust in machine learning technologies.

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