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、多跳查询和领域特定问答中面临重大挑战。尽管现有GraphRAG框架在结构化知识组织方面取得进展，但仍存在跨行业适应性不足、社区报告完整性欠缺和检索性能受限等问题。本文提出UniAI-GraphRAG，一个基于开源GraphRAG构建的增强框架。该框架包含三大核心创新：(1)本体引导知识抽取，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、基于属性的聚类和多跳关系聚类提升社区完整性；(3)双通道图检索融合，通过混合图与社区检索平衡问答准确性与性能。在MultiHopRAG基准上的评估结果表明，UniAI-GraphRAG在综合F1分数上优于主流开源方案（如LightRAG），尤其在推理类和时间类查询中表现突出。代码已开源：https://github.com/UnicomAI/wanwu/tree/main/rag/rag_open_source/rag_core/graph。