TM-RAG: A Tree-Mapped Retrieval-Augmented generation framework for construction claim report generation
Year: 2025
Type: article
Abstract: To improve project administration and ensure efficient claim management, it is essential to accurately document all claim-related information. To improve claim recording efficiency, this study proposes an automatic claim report generation method: Tree-Mapped Retrieval-Augmented Generation (TM-RAG). TM-RAG maintains a timeline ontology that represents the workflow of claim events and establishes structured mappings between ontology nodes and corresponding entries in claim reports based on historical data. For new claim cases, TM-RAG can rapidly instantiate the ontology structure, construct a hierarchical time tree (a graph-based structure), and accurately extract information for claim report generation, leveraging mapped relationships. Experimental results show that TM-RAG with a locally deployed Qwen2.5-7B model achieves the best overall score (0.937), substantially improving EM over vector-based (+21–23 %) and graph-based methods (+15–17 %), while delivering on average + 33 % semantic quality gains, at only ∼ 20 % of GraphRAG’s runtime. Moreover, TM-RAG is adaptable to different claim report formats and cross-format knowledge transfer. Beyond claim management, TM-RAG offers a new paradigm for graph-augmented RAG methods to handle large-scale, structurally repetitive information extraction tasks, thereby improving graph construction and retrieval efficiency by leveraging historical data.
Source: Advanced Engineering Informatics
Authors: Wentao Zhu, Xiao Li, Liang Wang, Juan Wang, Yinyi Wei
Institution: University of Hong Kong
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,560
Awards: G7000024347, G7371822428
OpenAlex ID: W7106366296