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 knowledge extraction precision, community report integrity, and retrieval performance. This paper proposes OMD-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 MultiHop-RAG benchmark show that OMD-GraphRAG outperforms mainstream open source solutions (e.g., LightRAG) in comprehensive F1 scores, particularly in inference and temporal queries.

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。现有图检索增强生成（GraphRAG）框架虽在结构化知识组织方面取得进展，但在知识提取精度、社区报告完整性和检索性能方面仍存在局限。本文提出OMD-GraphRAG——一种基于开源GraphRAG构建的增强框架。该框架引入三项核心创新：（1）本体引导的知识提取，利用预定义Schema引导大语言模型准确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）双通道图检索融合，通过混合图检索与社区检索平衡问答准确性与性能。在MultiHop-RAG基准上的评估表明，OMD-GraphRAG在综合F1分数上优于主流开源方案（如LightRAG），尤其在推理类与时间类查询中表现突出。