Graph-based RAG Enhancement via Global Query Disambiguation and Dependency-Aware Reranking

• URL: http://arxiv.org/abs/2506.11106v1
• Date: Sat, 07 Jun 2025 07:17:14 GMT
• Title: Graph-based RAG Enhancement via Global Query Disambiguation and Dependency-Aware Reranking
• Authors: Ningyuan Li, Junrui Liu, Yi Shan, Minghui Huang, Tong Li,
• Abstract summary: PankRAG is a globally aware, hierarchical query-resolution strategy with a novel dependency-aware reranking mechanism.<n>It applies its dependency-aware reranker to exploit the dependency structure among resolved sub-questions.<n>PankRAG consistently outperforms state-of-the-art approaches across multiple benchmarks.
• Score: 9.280502741892676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary graph-based retrieval-augmented generation (RAG) methods typically begin by extracting entities from user queries and then leverage pre-constructed knowledge graphs to retrieve related relationships and metadata. However, this pipeline's exclusive reliance on entity-level extraction can lead to the misinterpretation or omission of latent yet critical information and relations. As a result, retrieved content may be irrelevant or contradictory, and essential knowledge may be excluded, exacerbating hallucination risks and degrading the fidelity of generated responses. To address these limitations, we introduce PankRAG, a framework that combines a globally aware, hierarchical query-resolution strategy with a novel dependency-aware reranking mechanism. PankRAG first constructs a multi-level resolution path that captures both parallel and sequential interdependencies within a query, guiding large language models (LLMs) through structured reasoning. It then applies its dependency-aware reranker to exploit the dependency structure among resolved sub-questions, enriching and validating retrieval results for subsequent sub-questions. Empirical evaluations demonstrate that PankRAG consistently outperforms state-of-the-art approaches across multiple benchmarks, underscoring its robustness and generalizability.

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