Abstract. IFC models store detailed building information, but their complex schema and deeply nested property sets make querying difficult for non-expert users and challenging for large language models (LLMs) to handle directly. Current LLM-based approaches are inefficient because prompts often include entire IFC schemas, many properties of which are irrelevant to the user’s query, leading to higher inference costs and potential errors. This paper presents Query2Property, a semantic retrieval system that maps natural language queries to the most relevant IFC properties. By embedding both property descriptions and user queries in a shared vector space, the system retrieves contextually relevant properties for dynamic and concise prompt construction in LLM-driven workflows. Evaluation on 55 representative BIM queries achieves a top-1 accuracy of 87.3% and top-3 accuracy of 100%, demonstrating effective alignment with user intent. Query2Property simplifies LLM-based workflows over BIM data, supporting semantic search and natural language exploration of complex building information.
Rabindra Lamsal, S. Zlatanova· ISPRS Annals of the Photogra...· 0 citations
Semantic 3D city models provide rich geometric and semantic information, but remain challenging for non-experts and interdisciplinary researchers to access and query due to their complex structures and specialized data formats. To address this issue, we present CityLLM, a framework for natural-language querying of semantic 3D city models alongside complementary urban datasets. The framework combines spatial and graph databases within an LLM-based workflow that supports iterative query refinement and cross-database chaining. We evaluate CityLLM on a CityJSON dataset of Rotterdam (853 LoD2 buildings) using GPT-OSS, Gemini 3.1, and GPT-5.4, along with selected variants, across multiple metrics: answer correctness, visualization correctness, query success, and retry attempts. A total of 54 natural-language queries are curated across four scenarios: spatial, graph, cross-database, and conversational. Results show strong overall performance, with answer correctness ranging from 85.2% to 100%, visualization correctness from 92.9% to 100%, a 100% query success rate, and fewer than three retries across all 54 queries. Overall, the findings suggest that CityLLM provides a lightweight and extensible approach for conversational access to semantic 3D city data.
Rabindra Lamsal, S. Zlatanova, John Shen· 0 citations
