现代信息科技2026，Vol.10Issue(17)：58-65,8.DOI:10.19850/j.cnki.2096-4706.2026.17.011
面向ROS知识问答的层次化图检索增强生成方法
A Hierarchical Graph Retrieval-Augmented Generation Framework for ROS Knowledge Question Answering
施亚龙 1赵俊 1侯劲1
作者信息
- 1. 四川轻化工大学,四川 宜宾 644005
- 折叠
摘要
Abstract
To address the problems of context fragmentation and macro-micro structure disconnection in graph retrieval for the Robot Operating System(ROS),this paper proposes a Hierarchical Graph Retrieval-Augmented Generation framework(HiGraphRAG).The framework repairs physical connectivity faults through global graph reorganization,constructs a bottom-up hierarchical summary index based on Gaussian Mixture Models(GMM),and adopts a perspective expansion mechanism to realize the accurate recall of multi-granularity structured knowledge.Experimental results on a self-built ROS dataset show that compared with the mainstream GraphRAG paradigm,HiGraphRAG achieves an overall generation quality win rate of 56.4%and a knowledge diversity win rate of 60.2%.The framework effectively bridges system information gaps and significantly improves the comprehensiveness and knowledge empowerment of large language models in complex reasoning.关键词
检索增强生成/知识图谱/机器人操作系统/层次化索引/层次化检索Key words
Retrieval-Augmented Generation/Knowledge Graph/robot operating system/hierarchical index/hierarchical retrieval分类
信息技术与安全科学引用本文复制引用
施亚龙,赵俊,侯劲..面向ROS知识问答的层次化图检索增强生成方法[J].现代信息科技,2026,10(17):58-65,8.
