让施工项目决策历史可对话查询,一键追溯关键决定的演变过程。
Chronological Knowledge Retrieval: A Retrieval-Augmented Generation Approach to Construction Project Documentation

- 用检索增强生成框架,结合语义搜索与大模型理解会议纪要。
- 支持自然语言提问并返回带时间标注的答案,准确还原决策演变。
- 适用于工程管理者、项目协调员等需快速回溯历史决策的用户。
在大型施工项目中,决策持续演进产生大量记录,主要以会议纪要形式存档。由于新决策可能推翻旧决定,从业者常需重建特定决策的历史脉络。从用户角度,本文提出通过对话方式访问全部会议纪要,支持自然语言提问,并获得语义相关且明确标注时间的答案,帮助理解决策的时间顺序。技术上,采用检索增强生成(RAG)框架,融合语义搜索与大语言模型,确保回答准确且上下文感知。实验基于比利时一家大型公司提供的匿名化项目会议纪要数据集,该数据集经专家标注并扩充了定制查询,支持系统评估。相关数据集与开源实现已公开,旨在推动面向时序标注项目文档的对话式访问研究。
原文摘要 · Abstract (English)
In large-scale construction projects, the continuous evolution of decisions generates extensive records, most often captured in meeting minutes. Since decisions may override previous ones, professionals often need to reconstruct the history of specific choices. Retrieving such information manually from raw archives is both labor-intensive and error-prone. From a user perspective, we address this challenge by enabling conversational access to the whole set of project meeting minutes. Professionals can pose natural-language questions and receive answers that are both semantically relevant and explicitly time-annotated, allowing them to follow the chronology of decisions. From a technical perspective, our solution employs a Retrieval-Augmented Generation (RAG) framework that integrates semantic search with large language models to ensure accurate and context-aware responses. We demonstrate the approach using an anonymized, industry-sourced dataset of meeting minutes from a completed construction project by a large company in Belgium. The dataset is annotated and enriched with expert-defined queries to support systematic evaluation. Both the dataset and the open-source implementation are made available to the community to foster further research on conversational access to time-annotated project documentation.
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