Recent graph-based RAG approaches leverage knowledge graphs by extracting entities from a query to fetch their associated relationships and metadata. However, relying solely on entity extraction often results in the misinterpretation or omission of latent critical information and relationships. This can lead to the retrieval of irrelevant or contradictory content, as well as the exclusion of essential information, thereby increasing hallucination risks and undermining the quality of generated responses. In this paper, we propose PankRAG, a framework designed to capture and resolve the latent relationships within complex queries that prior methods overlook. It achieves this through a synergistic combination of a globally-aware hierarchical resolution pathway and a dependency-aware reranking mechanism. PankRAG first generates a globally aware resolution pathway that captures parallel and progress relationships, guiding LLMs to resolve queries through a hierarchical reasoning path. Additionally, its dependency-aware reranking mechanism utilizes resolved sub-question dependencies to augment and validate the retrieved content of the current unresolved sub-question. Experimental results demonstrate that PankRAG consistently outperforms existing state-of-the-art methods, underscoring its generalizability.

翻译：近期的基于图的检索增强生成方法通过从查询中提取实体来获取其关联关系和元数据，从而利用知识图谱。然而，仅依赖实体提取常常导致对潜在关键信息和关系的误解或遗漏。这可能引发检索到无关或矛盾的内容，以及排除关键信息，从而增加幻觉风险并损害生成响应的质量。本文提出PankRAG，一个旨在捕获和解析复杂查询中先前方法所忽视的潜在关系的框架。它通过全局感知的层次化解析路径与依赖感知的重排序机制的协同组合来实现这一目标。PankRAG首先生成一个全局感知的解析路径，该路径捕获并行和递进关系，引导大语言模型通过层次化推理路径来解析查询。此外，其依赖感知的重排序机制利用已解析子问题的依赖关系来增强和验证当前未解析子问题的检索内容。实验结果表明，PankRAG在各项评估中持续优于现有的最先进方法，突显了其良好的泛化能力。