Development of a GraphRAG-Based Automated Knowledge Graph Query Generation Model for Inference Search in BIM Data: A Case Study of a Rigid-Frame Bridge

Recently, Building Information Modeling (BIM) has evolved beyond three-dimensional modeling, increasingly focusing on the quality of standardized attribute data and systematic data management.Accordingly, ontology-and knowledge-graph-based data representation and reasoning approaches have increasingly gained attention.Knowledge-graph-based BIM data can support practical tasks such as structural relationship analysis, design review, and quantity and attribute information retrieval.However, practical adoption is limited by the need to manually write graph query languages, such as SPARQL and Cypher, which requires detailed knowledge of graph schemas and query syntax.To address this limitation, a GraphRAG-based automated query generation framework that converts natural-language questions into graph queries is proposed.A CSV-to-Labeled Property Graph (LPG) pipeline is developed to automate graph construction by generating nodes and relationships from rule-based attribute and relationship tables derived from bridge BIM data.In addition, a few-shot learning-based prompt design is utilized to generate Cypher queries from natural-language inputs automatically.The proposed framework is implemented as the Graph-ACQ system and validated using rigid-frame bridge BIM data.Experimental results demonstrate 100% accuracy in schema validity and query generation, with an average processing time of 7.1s.The proposed approach enables intuitive, relationship-oriented BIM data querying and supports scalable application across diverse infrastructure domains.