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

Ibba A.;Alonso R.;Reforgiato Recupero D.

2026-01-01

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Abstract

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.