Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH structure and semantics, which constrain their ability to perform effective multi-hop question answering. To overcome these limitations, we propose PRoH, a dynamic Planning and Reasoning over Knowledge Hypergraphs framework. PRoH incorporates three core innovations: (i) a context-aware planning module that sketches the local KH neighborhood to guide structurally grounded reasoning plan generation; (ii) a structured question decomposition process that organizes subquestions as a dynamically evolving Directed Acyclic Graph (DAG) to enable adaptive, multi-trajectory exploration; and (iii) an Entity-Weighted Overlap (EWO)-guided reasoning path retrieval algorithm that prioritizes semantically coherent hyperedge traversals. Experiments across multiple domains demonstrate that PRoH achieves state-of-the-art performance, surpassing the prior SOTA model HyperGraphRAG by an average of 19.73% in F1 and 8.41% in Generation Evaluation (G-E) score, while maintaining strong robustness in long-range multi-hop reasoning tasks.

翻译：知识超图（KHs）最近作为一种用于检索增强生成（RAG）的知识表示形式出现，它提供了一种将多实体关系建模为结构化形式的范式。然而，现有的基于KH的RAG方法存在三个主要局限性：静态的检索规划、非自适应的检索执行以及对KH结构和语义的浅层利用，这些限制了他们执行有效多跳问答的能力。为了克服这些局限性，我们提出了PRoH，一个基于知识超图的动态规划与推理框架。PRoH包含三项核心创新：（i）一个上下文感知规划模块，它勾勒出局部KH邻域以指导生成结构化的推理计划；（ii）一个结构化的问题分解过程，将子问题组织成一个动态演化的有向无环图（DAG），以实现自适应的、多轨迹探索；（iii）一种实体加权重叠（EWO）引导的推理路径检索算法，该算法优先考虑语义连贯的超边遍历。跨多个领域的实验表明，PRoH实现了最先进的性能，在F1分数上平均超过先前SOTA模型HyperGraphRAG达19.73%，在生成评估（G-E）分数上平均超过8.41%，同时在长距离多跳推理任务中保持了强大的鲁棒性。