Chronological Knowledge Retrieval: A Retrieval-Augmented Generation Approach to Construction Project Documentation
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Provenance Analysis of Archaeological Artifacts via Multimodal RAG Systems
In this work, we present a retrieval-augmented generation (RAG)-based system for provenance analysis of archaeological artifacts, designed to support expert reasoning by integrating multimodal retrieval and large vision-…
Semantic RetrievalDuetRAG: Collaborative Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks. However, contemporary RAG approaches …
PhilosophyRAGRetrievalRetrieval-augmented GenerationHeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations
Retrieval-augmented generation (RAG) methods can enhance the performance of LLMs by incorporating retrieved knowledge chunks into the generation process. In general, the retrieval and generation steps usually have differ…
RAGRetrievalRetrieval-augmented GenerationEnhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
Large language models are powerful text processors and reasoners, but are still subject to limitations including outdated knowledge and hallucinations, which necessitates connecting them to the world. Retrieval-augmented…
Fact VerificationMulti-hop Question AnsweringQuestion AnsweringRetrievalSimilarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their a…
AllHallucinationRAGRetrieval+1