A Hybrid Framework for Natural Language Querying of IFC Models with Relational and Graph Representations

• URL: http://arxiv.org/abs/2605.13236v1
• Date: Wed, 13 May 2026 09:24:51 GMT
• Title: A Hybrid Framework for Natural Language Querying of IFC Models with Relational and Graph Representations
• Abstract summary: We introduce IfcLLM, a hybrid framework for natural language interaction with IFC-based BIM models.<n>We implement the framework using an open-weight LLM (GPT OSS 120B), supporting reproducible and deployment-oriented.<n>The results show that combining complementary representations with iterative reasoning enables more accessible natural language querying of IFC data.
• Score: 1.7964861233408138
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Building Information Modeling (BIM) is widely used in the Architecture, Engineering, and Construction (AEC) industry, but the complexity of Industry Foundation Classes (IFC) limits accessibility for non-expert users. To address this, we introduce IfcLLM, a hybrid framework for natural language interaction with IFC-based BIM models. It transforms IFC models into complementary representations: a relational representation for structured element properties and geometry, and a graph representation for topological relationships. These representations are integrated through iterative retry-and-refine LLM reasoning. We implement the framework using an open-weight LLM (GPT OSS 120B), supporting reproducible and deployment-oriented workflows. Evaluation on three IFC models with queries derived from 30 scenarios shows first-attempt accuracy of 93.3%-100%, with all failures recovered using a fallback LLM. The results show that combining complementary representations with iterative reasoning enables more accessible natural language querying of IFC data while supporting routine BIM analysis tasks.

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