Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While existing GraphRAG frameworks have made progress in structural knowledge organization, they still have limitations in cross-industry adaptability, community report integrity, and retrieval performance. This paper proposes UniAI-GraphRAG, an enhanced framework built upon open-source GraphRAG. The framework introduces three core innovations: (1) Ontology-Guided Knowledge Extraction that uses predefined Schema to guide LLMs in accurately identifying domain-specific entities and relations; (2) Multi-Dimensional Community Clustering Strategy that improves community completeness through alignment completion, attribute-based clustering, and multi-hop relationship clustering; (3) Dual-Channel Graph Retrieval Fusion that balances QA accuracy and performance through hybrid graph and community retrieval. Evaluation results on MultiHopRAG benchmark show that UniAI-GraphRAG outperforms mainstream open source solutions (e.g.LightRAG) in comprehensive F1 scores, particularly in inference and temporal queries. The code is available at https://github.com/UnicomAI/wanwu/tree/main/rag/rag_open_source/rag_core/graph.

翻译：检索增强生成（RAG）系统在复杂推理、多跳查询及领域特定问答任务中面临显著挑战。尽管现有GraphRAG框架在结构化知识组织方面取得进展，但在跨行业适应性、社区报告完整性与检索性能上仍存在局限。本文提出UniAI-GraphRAG——一种基于开源GraphRAG的增强框架。该框架包含三项核心创新：（1）本体引导知识提取，通过预定义Schema引导大语言模型精准识别领域特定实体与关系；（2）多维社区聚类策略，通过对齐补全、属性聚类与多跳关系聚类提升社区完整性；（3）双通道图谱检索融合，结合图检索与社区检索平衡问答准确率与性能。在MultiHopRAG基准测试中，UniAI-GraphRAG的综合F1分数优于主流开源方案（如LightRAG），尤其在推理与时序查询任务中表现突出。完整代码发布于https://github.com/UnicomAI/wanwu/tree/main/rag/rag_open_source/rag_core/graph。