ASK-BIM: A knowledge graph-powered AI system for natural language querying of BIM models

Building Information Modeling (BIM) has transformed the Architecture, Engineering, Construction, and Operation (AECO) industry by integrating diverse types of information into a unified digital model. While BIM enhances collaboration and decision, making throughout a project's lifecycle, querying and extracting meaningful insights from BIM models remains a challenge due to their complexity and the technical expertise required to navigate formats like Industry Foundation Classes (IFC). Existing tools provide limited accessibility, and applying state-of-the-art Large Language Models (LLMs) directly to IFC files has proven ineffective due to data volume, lack of semantic structure, and relational complexity. To address these challenges, we introduce ASK-BIM, an approach that combines LLMs, linked data, and knowledge graph (KG) technologies to enable natural language querying of IFC files. By structuring BIM data into a KG before engaging an LLM for reasoning and query resolution, ASK-BIM enhances data accessibility while preserving semantic relationships crucial for complex queries. We evaluate ASK-BIM on a real-world multi-storey building, categorizing questions along two axes and assessing performance in extracting relevant information. Our findings demonstrate the potential of graph-based representations to facilitate AI-driven BIM analysis while also identifying challenges related to the extraction of information from the graph structures. By bridging the gap between BIM data and AI reasoning, ASK-BIM represents a significant step toward intuitive and efficient BIM querying through natural language.