Academic

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

arXiv:2603.14006v1 Announce Type: new 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 mu

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Hang Gao, Dimitris N. Metaxas · · 1 min read · 6 views
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arXiv:2603.14006v1 Announce Type: new 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.

Executive Summary

This article introduces INSES, a dynamic framework for robust reasoning over noisy and sparse knowledge graphs. INSES combines LLM-guided navigation and embedding-based similarity expansion to recover hidden links and bridge semantic gaps. The framework is complemented by a lightweight router that balances efficiency with reasoning depth. INSES outperforms state-of-the-art RAG and GraphRAG baselines across multiple benchmarks, demonstrating superior robustness and accuracy on the MINE benchmark. The article's contributions and findings have significant implications for the development of more efficient and effective graph reasoning algorithms, particularly in real-world scenarios where knowledge graphs are incomplete or noisy.

Key Points

  • ▸ INSES introduces a dynamic framework for robust reasoning over noisy and sparse knowledge graphs.
  • ▸ The framework combines LLM-guided navigation and embedding-based similarity expansion to recover hidden links and bridge semantic gaps.
  • ▸ INSES is complemented by a lightweight router that balances efficiency with reasoning depth.

Merits

Strength in Addressing Noisy and Sparse Knowledge Graphs

INSES effectively addresses the limitations of standard graph algorithms, which rely heavily on static connectivity and explicit edges, by incorporating dynamic and adaptive reasoning mechanisms.

Superior Robustness and Accuracy

INSES consistently outperforms state-of-the-art RAG and GraphRAG baselines across multiple benchmarks, demonstrating superior robustness and accuracy on the MINE benchmark.

Demerits

High Computational Cost

The LLM-guided navigation and embedding-based similarity expansion components of INSES may incur significant computational costs, which could be a limitation in real-world applications.

Dependence on Large Language Models

INSES relies on large language models (LLMs) for navigation and similarity expansion, which may introduce additional complexity and dependencies.

Expert Commentary

The article introduces a novel and innovative framework for robust reasoning over noisy and sparse knowledge graphs. INSES's combination of LLM-guided navigation and embedding-based similarity expansion offers a promising approach to addressing the limitations of standard graph algorithms. However, the high computational cost and dependence on large language models may be significant limitations in real-world applications. Future work should focus on addressing these limitations and exploring the practical and policy implications of INSES and similar frameworks.

Recommendations

  • ✓ Further research is needed to investigate the scalability and efficiency of INSES in real-world applications.
  • ✓ The development of more efficient and effective graph reasoning algorithms, such as INSES, should be prioritized to address the limitations of current graph algorithms.

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