Abstract
IFC data has become the general building information standard for collaborative work in the construction industry. However, IFC data can be very complicated because it allows for multiple ways to represent the same product information. In this research, we utilise the capabilities of LLMs to parse the IFC data with Graph Retrieval-Augmented Generation (Graph-RAG) technique to retrieve building object properties and their relations. We will show that, despite limitations due to the complex hierarchy of the IFC data, the Graph-RAG parsing enhances generative LLMs like GPT-4o with graph-based knowledge, enabling natural language query-response retrieval without the need for a complex pipeline.
Abstract (translated)
IFC数据已成为建筑业中协作工作的通用建筑信息标准。然而，由于允许以多种方式表示相同的产品信息，IFC数据可以变得非常复杂。在这项研究中，我们利用大型语言模型（LLM）的能力，采用图检索增强生成技术（Graph-RAG），来解析IFC数据并提取建筑物对象的属性及其关系。我们将展示，在面对IFC数据复杂的层级结构所导致的限制时，Graph-RAG解析能够通过基于图形的知识增强像GPT-4o这样的生成型LLM，从而实现自然语言查询响应检索，而无需复杂的管道支持。
URL
https://arxiv.org/abs/2504.16813
