GraphRAG is increasingly adopted for converting unstructured corpora into graph structures to enable multi-hop reasoning. However, standard graph algorithms rely heavily on static connectivity and explicit edges, often failing in real-world scenarios where knowledge graphs (KGs) are noisy, sparse, or incomplete. To address this limitation, we introduce INSES (Intelligent Navigation and Similarity Enhanced Search), a dynamic framework designed to reason beyond explicit edges. INSES couples LLM-guided navigation, which prunes noise and steers exploration, with embedding-based similarity expansion to recover hidden links and bridge semantic gaps. Recognizing the computational cost of graph reasoning, we complement INSES with a lightweight router that delegates simple queries to Naïve RAG and escalates complex cases to INSES, balancing efficiency with reasoning depth. INSES consistently outperforms SOTA RAG and GraphRAG baselines across multiple benchmarks. Notably, on the MINE benchmark, it demonstrates superior robustness across KGs constructed by varying methods (KGGEN, GraphRAG, OpenIE), improving accuracy by 5%, 10%, and 27%, respectively.

翻译：GraphRAG 正日益广泛地应用于将非结构化语料库转换为图结构，以实现多跳推理。然而，标准图算法严重依赖静态连通性和显式边，在现实场景中，当知识图谱（KGs）存在噪声、稀疏或不完整时，其性能往往不佳。为应对这一局限，我们提出了 INSES（智能导航与相似性增强搜索），这是一个旨在超越显式边进行推理的动态框架。INSES 将 LLM 引导的导航（用于剪枝噪声并引导探索）与基于嵌入的相似性扩展相结合，以恢复隐藏链接并弥合语义鸿沟。考虑到图推理的计算成本，我们为 INSES 配备了一个轻量级路由器，该路由器将简单查询委托给 Naïve RAG，并将复杂案例升级至 INSES，从而在效率与推理深度之间取得平衡。在多个基准测试中，INSES 持续优于最先进的 RAG 和 GraphRAG 基线。值得注意的是，在 MINE 基准测试中，它展示了在不同方法（KGGEN、GraphRAG、OpenIE）构建的知识图谱上均具有卓越的鲁棒性，准确率分别提升了 5%、10% 和 27%。