BIM-GRASP: A Graph-RAG Approach for Semantic Parsing of IFC Building Models

Abstract

Building Information Modeling (BIM) is central to modern construction and design, with the Industry Foundation Classes (IFC) format serving as a widely adopted open standard for representing building models. However, IFC data is notoriously complex as it encodes structural, geometric, and semantic information in deeply nested relationships that are difficult to interpret without specialized tools and domain expertise. This complexity limits access to meaningful insights, particularly for non-technical stakeholders. We introduce BIM-GRASP, a novel system-level application that combines Graph-Retrieval Augmented Generation (Graph-RAG) with Generative AI to enable natural language interaction with IFC building models. BIM-GRASP transforms IFC files into a knowledge graph, which is then queried by a Large Language Model (LLM) to answer questions about building elements such as geometric attributes, material specifications, and inter-element relationships. Unlike traditional IFC parsers, BIM-GRASP supports complex queries that require reasoning across multiple layers of the IFC hierarchy as it is able to retrieve information from disparate parts of the model. This allows users to ask questions and receive accurate, context-aware answers in natural language, without relying on schema-level navigation or technical syntax. Our findings show that providing the model with domain-specific In-Context Learning (ICL) significantly improves precision and relevance in information extraction, achieving an average accuracy of 88% across diverse building models and query types. To the best of the authors’ knowledge, BIM-GRASP is the first framework of its kind to enable the parsing of IFC data as a knowledge graph through natural language without requiring specialized technical expertise or manual schema navigation. By bridging the gap between complex building data and non-technical stakeholders, BIM-GRASP accelerates decision-making and improves transparency across the built environment.