R e G raph RAG : Reorganizing Fragmented Knowledge Graphs for Multi-Perspective Retrieval-Augmented Generation (2025)

Abstract: Recent advancements in Retrieval-Augmented Generation (RAG) have improved large language models (LLMs) by incorporating external knowledge at inference time. Graph-based RAG systems have emerged as promising approaches, enabling multi-hop reasoning by organizing retrieved information into structured graphs. However, when knowledge graphs are constructed from unstructured documents using LLMs, they often suffer from fragmentation—resulting in disconnected subgraphs that limit inferential coherence and undermine the advantages of graph-based retrieval. To address these limitations, we propose ReGraphRAG, a novel framework designed to reconstruct and enrich fragmented knowledge graphs through three core components: Graph Reorganization, Perspective Expansion, and Query-aware Reranking. Experiments on four benchmarks show that ReGraphRAG outperforms state-of-the-art baselines, achieving over 80% average diversity win rate. Ablation studies highlight the key contributions of graph reorganization and especially perspective expansion to performance gains. Our code is available at: https://anonymous.4open.science/r/ReGraphRAG-7B73

## introduction recent advances in artificial intelligence (ai), particularly the emergence and widespread adoption of large language models (llms) @xcite @xcite , have significantly transformed the landscape of natural language processing and knowledgeintensive tasks. while llms have demonstrated impressive capabilities across a wide range of applications, their reliance on static, pre-trained knowledge introduces limitations when addressing queries that require up-to-date, domain-specific, or contextually nuanced information @xcite * corresponding author 2023). these challenges have spurred the development and adoption of retrieval-augmented generation (rag) frameworks @xcite , which enhance the reasoning and generation capabilities of llms by dynamically incorporating external information from large-scale knowledge sources during inference. building upon the foundational paradigm of rag, graph-based rag has emerged as a prominent structured extension that organizes retrieved information into graph structures @xcite @xcite . unlike conventional rag methods that treat retrieved information as independent text chunks, graph-based rag advances this framework by retrieving and organizing knowledge in the form of graphs, thereby supporting multi-hop reasoning and improved contextual coherence. notably, contemporary approaches have moved beyond relying solely on pre-existing knowledge graphs; instead, they employ llms to construct knowledge graphs directly from unstructured documents, enabling more flexible and adaptive knowledge integration @xcite . this graph-based retrieval and reasoning mechanism not only strengthens the logical flow of generated responses but also contributes significantly to the overall quality and reliability of llms outputs. despite the growing interest in graph-based rag and the emergence of various methodological advancements, significant limitations remain unaddressed. a key challenge lies in the fragmentation that arises when indexing documents into knowledge graphs. when llms are used to extract structured information from unstructured text, the resulting graphs often consist of numerous disconnected nodes and isolated components, resulting in sparse and incoherent structures. this undermines the core strength of graph-based retrieval-its ability to support multi-hop reasoning across semantically linked entities. consequently, as shown in figure 1 (a), the retrieval process itself becomes disjointed, diminishing the effectiveness of downstream reasoning and generation. to address this issue, we propose regraphrag, a novel framework that reorganizes retreived fragmented knowledge into connected graphs, thereby enhancing the comprehensiveness and inferential depth of generated responses. regraphrag, a novel framework comprising three key components: graph reorganization, perspective expansion, and query-aware reranking. first, as shown in figure 1(b) , graph reorganization ensures that the retrieved subgraphs are transformed into a single connected graph by identifying meaningful shortest paths between them. this process not only bridges missing connections among disjoint subgraphs but also introduces new edges to support coherent reasoning flows, thereby enhancing both the logical consistency and inferential strength of the generated answers. given that this reorganization guarantees a connected structure, we further enhance the breadth of retrieved knowledge through perspective expansion, which decomposes the original query into multiple interpretive angles and gen-erates sub-queries accordingly. each sub-query guides the retrieval of complementary subgraphs that collectively broaden the semantic coverage. finally, all retrieved and reorganized information is converted into a structured triplet form (< node i , edge ij , node @xmath0, which scores and rerank the most relevant triplets based on their semantic similarity to the original query. one of the core contributions of this work is the introduction of regraphrag, a novel framework that reconstructs knowledge graphs from multiple interpretive perspectives based on the input query. this approach enables the generation of contextually enriched and structurally coherent graphs that are well-suited for inference-driven answer generation. experimental evaluations conducted on four benchmark datasets demonstrate that regraphrag consistently outperforms stateof-the-art baselines across all four evaluation dimensions-comprehensiveness, diversity, empowerment, and overall. in particular, the average diversity win rate exceeds 80% compared to existing methods, highlighting the framework's strong capacity to capture and integrate semantically diverse information. furthermore, ablation studies and indepth analyses reveal that both graph reorganization and perspective expansion are key contributors to performance gains. notably, perspective expansion is shown to produce significant improvements, underscoring the importance of multi-perspective reasoning in enhancing the quality of generated responses. ## background graph-based rag extends the traditional rag framework by replacing or enriching flat text indices with explicit graph structures that capture entities and their interrelationships. to construct such graphs from input text documents, llms are employed to recognize and extract entities and the relationships among them, resulting in an entity set v and a relationship set e * . specifically, the resulting knowledge graph, constructed from textual data, is denoted as @xmath1, e) and consists of k subgraphs. each node @xmath2, description, and type. each edge @xmath3, including metadata such as the source and target entities and a textual description of the relation. all nodes and edges are embedded into a vector space using a pre-trained embedding model, yielding vector representations φ(v) and φ(e), respectively. given a query q, the retriever r identifies semantically relevant nodes and edges from the knowledge graph g based on similarity measures. these retrieved nodes and edges, along with their associated metadata, are integrated into a predefined prompt template p , which, together with q, serves as input to a llm. this process can be formally represented as: in this paper, we focus on reorganizing the fragmented granularities retrieved from the graph to enhance the structural connectivity-one of the key advantages of the graph-based approach. furthermore, we aim to integrate multiple interpretive perspectives to generate responses that are enriched with diverse and contextually grounded knowledge. ## method as illustrated in figure 2 , regraphrag comprises three core components: perspective expansion (section 4.1), which decomposes the input query into multiple interpretive angles to retrieve diverse granularities (nodes and edges); graph reorganization (section 4.2), which reconstructs the fragmented subgraphs into a single connected graph structure; and query-aware reranking (section 4.3), which reorders the retrieved triplets in alignment with the query's semantic intent. finally, the reorganized graph is converted into a graphoriented prompt (section 4.4), which serves as the input to the language model for answer generation. ## conclusion in this work, we presented regraphrag, a novel graph-based rag framework designed to address the fragmentation problem inherent in llm-generated knowledge graphs. by integrating three key components-perspective expansion, graph reorganization, and query-aware reranking-regraphrag reconstructs and enriches disconnected knowledge graphs into coherent and inference-friendly structures. our extensive experiments on diverse benchmark domains demonstrate that regraphrag consistently outperforms strong baselines in all evaluation dimensions, especially achieving substantial gains in diversity and empowerment.