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

Hang Gao, Dimitris N. Metaxas

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 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.

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

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 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.
Paper Structure (46 sections, 8 equations, 4 figures, 14 tables, 2 algorithms)

This paper contains 46 sections, 8 equations, 4 figures, 14 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Knowledge Graph Reasoning
  4. Retrieval Augmented Generation
  5. Methodology
  6. Dynamic Reasoning vs. Static Completion
  7. Preliminaries
  8. Step 1: Extract Initial Entity Nodes
  9. Step 2: LLM Navigation
  10. Step 3: Similarity-based Expansion and Augmentation
  11. Complexity Control and Routing
  12. Experiments
  13. Datasets
  14. Baselines
  15. Metrics
  16. ...and 31 more sections

Figures (4)

  • Figure 1: An example of INSES workflow. Solid edges denote explicit relations; dashed edges denote dynamically added similarity edges. Nodes/edges with green background aid answering query. LLM maps query entities (“Greenwood Laboratory School” and “The Poor Boob”) to initial nodes, picks relevant triples while pruning noise, and uses similarity expansion to recover latent links (e.g., “Springfield” → “Springfield Illinois”). Navigation and pruning discard spurious edges, while expansion reveals critical connections, together enabling more reliable multi-hop reasoning.
  • Figure 2: Proportion of queries routed to Naïve RAG vs. INSES across three datasets.
  • Figure 3: Accuracy distributions comparison across INSES on KGs built by different methods.
  • Figure 4: Quantitative comparison of knowledge graph topologies generated by GraphRAG, KGGEN, and OpenIE on the MINE dataset (averaged over 100 articles). The significant variance in node/edge counts and graph density underscores the heterogeneity of real-world KGs, necessitating the adaptive navigation and dynamic expansion capabilities of INSES to handle both sparsity and noise.

Theorems & Definitions (2)

  • Definition 3.1
  • Definition 3.2