Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs

TL;DR

This paper introduces INSES, a dynamic reasoning framework that enhances knowledge graph navigation by combining LLM-guided exploration with similarity expansion, effectively handling noisy, sparse, or incomplete graphs and outperforming existing methods.

Contribution

INSES is a novel framework that integrates LLM-guided navigation with embedding-based similarity to improve reasoning over imperfect knowledge graphs.

Findings

Outperforms SOTA RAG and GraphRAG baselines across multiple benchmarks.

Demonstrates robustness with accuracy improvements of 5%, 10%, and 27% on the MINE benchmark.

Balances efficiency and reasoning depth with a lightweight routing mechanism.

Abstract

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 Na\"ive 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.