用双图结构提升建筑安全法规多跳问答准确率
Bridging Dual Knowledge Graphs for Multi-Hop Question Answering in Construction Safety
- 构建双知识图谱融合语义与文档结构
- 多跳问答F1达87.3%，显著优于单一检索方式
- 适合需要精准解析技术文档的工程合规场景
从安全规范中进行信息检索与问答是实现建筑合规自动化检查的关键，但监管文本的语言和结构复杂性导致多跳查询难以处理。为此，本文提出BifrostRAG——一种双图检索增强生成系统，同时建模语言关系与文档结构。该架构采用混合检索机制，结合图遍历与向量语义搜索，使大模型能基于内容与结构双重信息进行推理。在多跳问题数据集上，BifrostRAG实现92.8%精度、85.5%召回率和87.3% F1分数，显著优于仅向量或仅图的RAG基线，验证了其作为大模型驱动合规检查的知识引擎可靠性。本文提出的双图混合检索机制可推广至其他知识密集型工程领域中的复杂技术文档导航。
原文摘要 · Abstract (English)
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.
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