连接双知识图谱以实现施工安全多跳问答
Bridging Dual Knowledge Graphs for Multi-Hop Question Answering in Construction Safety
AI总结：
本文提出BifrostRAG系统，通过双图检索增强生成模型处理施工安全多跳问答，实现92.8%精度和85.5%召回率，优于传统基线方法。
摘要：
信息检索和从安全法规中进行问答对于自动化施工合规检查至关重要，但受到法规文本语言和结构复杂性的阻碍。许多查询需要跨相关条款的综合处理。为此，本文引入BifrostRAG，一种双图检索增强生成（RAG）系统，同时建模语言关系和文档结构。所提出的架构支持混合检索机制，结合图遍历与向量基于语义搜索，使大语言模型能够推理文本的内容和结构。在多跳问答数据集上，BifrostRAG实现了92.8%的精度、85.5%的召回率和87.3%的F1分数。这些结果显著优于仅向量和仅图的RAG基线，确立了BifrostRAG作为LLM驱动合规检查的稳健知识引擎。本文提出的双图混合检索机制为在知识密集型工程领域中导航复杂技术文档提供了可转移的蓝图。
英文摘要：
Information retrieval and question answering from safety regulations are essential for automated construction compliance checking but are hindered by the linguistic and structural complexity of regulatory text. Many queries are multi-hop, requiring synthesis across interlinked clauses. To address the challenge, this paper introduces BifrostRAG, a dual-graph retrieval-augmented generation (RAG) system that models both linguistic relationships and document structure. The proposed architecture supports a hybrid retrieval mechanism that combines graph traversal with vector-based semantic search, enabling large language models to reason over both the content and the structure of the text. On a multi-hop question dataset, BifrostRAG achieves 92.8% precision, 85.5% recall, and an F1 score of 87.3%. These results significantly outperform vector-only and graph-only RAG baselines, establishing BifrostRAG as a robust knowledge engine for LLM-driven compliance checking. The dual-graph, hybrid retrieval mechanism presented in this paper offers a transferable blueprint for navigating complex technical documents across knowledge-intensive engineering domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
