A cost-effective approach for knowledge graph reasoning path retrieval and enhanced large language model reliability

Large Language Models (LLMs) often face challenges in performing reliable multi-hop reasoning due to issues such as incomplete evidence chains and hallucinations. Incorporating knowledge graphs (KGs) can mitigate these problems, but existing approaches either suffer from suboptimal accuracy or are computationally expensive. To address these issues, we propose Reasoning Path Retrieval for RAG (RPR-RAG), a novel KG-based retrieval framework that incrementally builds a subgraph from the knowledge graph, extracts explicit reasoning paths, and provides them as structured external evidence to downstream LLMs. The experimental results on WebQuestionsSP (WebQSP) and Complex WebQuestions (CWQ) indicate that RPR-RAG achieves competitive Hit and F1 in multi-hop reasoning tasks, while maintaining runtime, LLM call frequency, and token usage at reasonable levels. Moreover, without additional task-specific training, RPR-RAG also shows strong zero-shot performance on MetaQA. RPR-RAG is built on a lightweight embedding model which can be trained and executed on a single consumer-grade GPU ( e.g ., RTX 3060, 6 GB). Ablation studies reveal that the path validity evaluation and stopping criterion play important roles in retrieval quality and efficiency. RPR-RAG is compatible with a range of backbone LLMs, from smaller 7B models to larger models such as GPT-5, providing a practical and interpretable framework for KG-grounded reasoning tasks. The source code is available at https://doi.org/10.5281/zenodo.19334059 .