Title: HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Retrieval-Augumented Generation

Hypergraph

N-ary Relation Extraction

Knowledge Representation
Abstract: Standard Retrieval-Augmented Generation (RAG) relies on chunk-based retrieval, whereas GraphRAG advances this approach by graph-based knowledge representation. However, existing graph-based RAG approaches are constrained by binary relations, as each edge in an ordinary graph connects only two entities, limiting their ability to represent the n-ary relations (n >= 2) in real-world knowledge. In this work, we propose HyperGraphRAG, the first hypergraph-based RAG method that represents n-ary relational facts via hyperedges. HyperGraphRAG consists of a comprehensive pipeline, including knowledge hypergraph construction, retrieval, and generation. Experiments across medicine, agriculture, computer science, and law demonstrate that HyperGraphRAG outperforms both standard RAG and previous graph-based RAG methods in answer accuracy, retrieval efficiency, and generation quality.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the problem of retrieval augmented generation with hypergraphs. Different from vanilla RAG methods, which mainly focus on chunk-based retrieval, in this work, the author proposes to construct hypergraphs based on the knowledge sources and conduct retrieval on such structured knowledge. The retrieved sub-hypergraphs are adopted to augment the generation for question answering. To be specific, the author proposes a pipeline for knowledge hypergraph construction, hypergraph retrieval, and hypergraph-guided generation. Experiments are conducted on several domains, where hypergraphRAG demonstrates its effectiveness and efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n- The paper is very well written and easy to follow.\n- The idea of constructing a hypergraph and augmenting the LLM with such structured knowledge makes sense.\n- Experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method.\n\nWeakness\n- The main concerns lie in the evaluation setup. It seems to me that the knowledge sources from each domain are just several documents, and this is quite different to real-world scenarios.\n- It is questionable whether the method can be generalized to scenarios where we have a much bigger knowledge source (e.g., wikipedia), and there may also be efficiency problems if we would like to construct a hypergraph from a big corpus.\n- Since the question samples are constructed by the author themselves and there is no clear explanation for this process, I would challenge if the question construction itself benefits the hypergraphRAG and thus leading to better performance comparing with baseline methods.\n- Some technique details are not well explained: (1) It is unclear how v^{score} in line 145 and e^{score}_H in line 153 are calculated. (2) It is questionable how the similarity calculation between two vectors of different dimensions (h_{V_q} and h_v) is performed in line 143.\n\nQUESTIONS:\nSee the weakness section above.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces HyperGraphRAG, a retrieval-augmented generation (RAG) framework that represents domain knowledge using hypergraphs to capture complex, n-ary relationships between entities. \nHyperGraphRAG consists of a three-stage pipeline: knowledge hypergraph construction, retrieval, and generation. First, it uses a large language model (LLM) to perform n-ary relation extraction from source documents, creating hyperedges that connect multiple entities. This hypergraph is then stored as a bipartite graph for efficient querying and vector embeddings are created for semantic retrieval. The retrieval strategy involves extracting entities from a user's question, searching for relevant entities and hyperedges, and then expanding the retrieved set to include related facts from the hypergraph. Finally, a hypergraph-guided generation mechanism fuses these structured n-ary facts with traditional chunk-based passages to produce the final answer. \nThe authors evaluate HyperGraphRAG on datasets from several domains and compare it against naive generation, standard RAG, and several graph-based RAG baselines. The results show that HyperGraphRAG consistently outperforms these methods in terms of answer accuracy (F1 score), retrieval similarity (R-S), and generation evaluation (G-E).\n\nSTRENGTHS AND WEAKNESSES:\nS1: The paper introduces a novel and intuitive approach by using hypergraphs to represent n-ary relations in knowledge sources, addressing a key limitation of previous GraphRAG methods that are restricted to binary relations. This allows for a more complete and expressive representation of complex, real-world knowledge.\n\nS2: The authors performed extensive and rigorous evaluation to show the advantage of the proposed method, evaluating on datasets from multi-domains and comparing against baselines of different methodologies. They also validates the architectural choices through a systematic ablation study, which helps understand the contribution of each component.\n\nS3: The paper is well presented and the source codes are open sourced, which helps reproduction of the proposed method and experiment results.\n\nW1: The evaluation benchmark is constructed by first sampling knowledge facts from the documents and then using an LLM to generate corresponding question-answer pairs. This synthetic generation process may create questions that are too neatly aligned with the structure of the knowledge graph, potentially favoring methods that are designed to traverse these explicit structures. \n\nW2:  The competing graph-based RAG methods consistently underperform the StandardRAG baseline, which might suggest that the implementations or hyperparameters used for these baselines were not fully optimized, or the datasets are construct in the favor of the proposed method.\n\nQUESTIONS:\nQ1: Following W1, do you ever try to evaluate the method on existing QA datasets such as those used by HippoRAG2? How does HyperGraphRAG compare against other graph-based RAG methods on their datasets?\n\nQ2:  The results in Table 2 show that the graph-based baselines consistently underperform the simpler StandardRAG method. This is a counter-intuitive finding. Could you provide more detail on the implementation and hyperparameter tuning for these baselines to ensure fair comparison and to help the reader understand why these methods failed to show an advantage in this setting?\n\nLIMITATIONS:\nNo, the authors have not adequately addressed the limitations of their work.\n- Potential for Evaluation Bias: The authors should acknowledge that generating evaluation questions from the knowledge graph's own facts may create a benchmark that favors graph-centric methods. Discussing this potential bias and its effect on the reported performance gains would provide important context.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper identifies a key limitation in existing RAG paradigms: standard methods ignore relational structure, while graph-based approaches are confined to binary relationships. This work assumes that such models are insufficient for capturing the complex, n-ary facts prevalent in real-world knowledge domains.\n\nTo address this limitation, the paper introduces HyperGraphRAG, a novel framework that leverages hypergraphs for knowledge representation. The core contribution is the modeling of n-ary facts as single hyperedges, each connecting multiple entities, thereby preserving the contextual integrity of complex information that would otherwise be fragmented. The authors present a complete methodology that includes an LLM-driven process for knowledge hypergraph construction, a dual-retrieval strategy for both entities and hyperedges, and a final hypergraph-guided generation stage.\n\nThe effectiveness of HyperGraphRAG is validated through extensive experiments across 5 distinct domains. The results demonstrate significant improvements over both standard RAG and existing graph-based baselines across multiple metrics, including answer accuracy and retrieval quality.\n\nSTRENGTHS AND WEAKNESSES:\n# Strength\n\n1. **Novelty of the Idea:** The paper's primary contribution is the application of hypergraphs to RAG, directly addressing a critical shortcoming of prior methods. By modeling complex facts as single hyperedges described in natural language, the approach preserves the contextual integrity of information. This is a significant conceptual advance, offering a new paradigm for knowledge representation.\n\n2. **Experiments:** The validation across five distinct domains and a fair comparison against a comprehensive suite of strong baselines provides compelling evidence for the method's superiority and generalizability.\n\n# Weakness\n\n1. **Systemic Risks Arising from the Method:** While conceptually novel, the proposed method makes a significant trade-off: it sacrifices the structural rigor, query precision, and inferential capabilities of traditional knowledge engineering for the flexibility of automated context extraction. This dependency on unstable LLM-based extraction introduces a systemic risk to the knowledge base's quality and consistency. Consequently, the system functions more as a sophisticated context retriever than as a knowledge base with precise and logical representations.\n\n2. **Challenges in Engineering Feasibility and Scalability:** The end-to-end pipeline is complex, and the associated costs of construction and maintenance are substantial. The heavy reliance on expensive LLMs for extraction and the long, multi-stage processing chain present significant challenges to scalability.\n\n3. **Limitations in Representational Capabilities:** The model's flat hyperedge structure is unsuitable for representing hierarchical or interdependent knowledge. While effective at bundling a single **event**, it struggles to model the complex logical relationships (e.g., causality, dependency, conditionality) that often connect these events in domains like law or science. This limits its applicability for tasks requiring deep, multi-step reasoning.\n\n4. **Absence of Knowledge Discovery Capabilities:** The framework's definition of knowledge is confined to explicit statements within the source text. It tends to reproduce existing information rather than discovering implicit patterns or latent relationships that are not explicitly stated. The resulting knowledge base is effectively a structured recombination of text snippets, not a higher-level abstraction of the underlying knowledge, thereby missing a key value proposition of traditional knowledge graphs: data mining and insight discovery.\n\nQUESTIONS:\nsee weakness\n\nLIMITATIONS:\nsee weakness", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces HyperGraphRAG, a novel retrieval-augmented generation framework leveraging hypergraph-structured knowledge representation to effectively model n-ary relationships involving multiple entities, thus overcoming the limitations of chunk-based and traditional binary graph-based methods. It consists of three key components: (1) knowledge hypergraph construction, which employs LLM-based extraction of multi-entity relational facts; (2) hypergraph retrieval, using semantic similarity to efficiently fetch relevant entities and hyperedges; and (3) hypergraph-guided generation, integrating structured hypergraph knowledge with traditional retrieval methods to enhance response accuracy and informativeness. The authors demonstrate through experiments across diverse domains—including medicine, agriculture, computer science, law, and a mixed domain—that HyperGraphRAG consistently outperforms existing retrieval methods in answer accuracy, retrieval relevance, and generation quality. These results confirm its practical effectiveness and broad applicability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\nQuality: The method rigorously addresses the key limitation of binary graph-based representations by introducing hypergraph structures, effectively capturing complex multi-entity relationships. The comprehensive theoretical justification, including formal proofs, strengthens the technical validity.\n\nClarity: The paper is clearly written, well-structured, and effectively communicates complex ideas with detailed explanations and illustrative figures. The step-by-step breakdown of methodology and experiments greatly enhances readability.\n\nSignificance: HyperGraphRAG addresses a meaningful and significant gap in current retrieval-augmented generation methods. By modeling n-ary relations, it substantially improves the completeness and informativeness of retrieved knowledge, which is critical for knowledge-intensive applications.\n\nOriginality: HyperGraphRAG is innovative, clearly differentiated from existing chunk-based and binary graph-based methods. The introduction of a hypergraph-guided generation strategy is particularly novel and practical.\n\n\nWeaknesses: \n1. Scalability concerns for complex reasoning tasks. HyperGraphRAG’s hypergraph structure, at inference time, only retrieves the vector-matched nodes and hyperedges plus their 1-hop neighbors, so direct entity-to-entity interactions are missing.\n2. Heavy workload for very long documents. In Appendix A.1, the N-ary relation extraction prompt asks the LLM to segment the input into coherent knowledge fragments and identify entities—but when documents become extremely long there is no post-processing to ensure that the model’s structured outputs remain well-formed.\n3. Questionable summarization and abstraction ability. In Algorithm 1, HyperGraphRAG handles each document independently before merging into a single hypergraph. However, because knowledge segments are extracted in isolation, it lacks GraphRAG’s community-based graph structuring and hierarchical summarization.\n4. Evaluation setup concerns. In section E, the Unified Generation Prompt requires GPT-4o-mini to “think” inside a <think> block before giving its answer in an <answer> block—but GPT-4o-mini is a short-CoT model, not a long-CoT reasoning LLM like DeepSeek-R1, which is designed to perform more extensive chain-of-thought before answering.\n5. The ablation studies were conducted primarily within the medicine domain. Providing additional ablation results in other domains would strengthen the empirical validation and generalizability claims.\n\nQUESTIONS:\n1. In HyperGraphRAG, hypergraphs are stored in a standard graph database using bipartite graph structures(sec 4.1). While this ensures compatibility with existing graph databases, it potentially introduces additional complexity or inefficiency in querying and maintaining high-order relationships. Could the authors elaborate on the rationale behind choosing a standard graph database instead of a native hypergraph database[1], and discuss any trade-offs or limitations (e.g., information loss, query efficiency, scalability)? Moreover, under what circumstances would the use of a native hypergraph database be beneficial or recommended for HyperGraphRAG?\n2.Does HyperGraphRAG have the potential to generalize to multi-hop reasoning scenarios?\n3.How does the N-ary Relation Extraction Prompt handle extremely long documents?\n4.How can one address failure cases caused by unstable structured output?\n5.What about using a long-CoT reasoning LLM (e.g. DeepSeek-R1 or its distilled version) as the QA model, since those are better at thinking through complex reasoning before answering?\n\n\n[1] Feng, Y., Hu, H., Hou, X., Liu, S., Ying, S., Du, S., ... & Gao, Y. (2025). Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation. arXiv preprint arXiv:2504.08758.\n\nLIMITATIONS:\nYes. The authors have thoroughly addressed key limitations, clearly highlighting areas for potential improvement.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}