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LLM-assisted Graph-RAG Information Extraction from IFC Data
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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.
Forward citations
Cited by 2 Pith papers
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IfcLLM: Natural Language Querying of IFC Models through Complementary Relational and Graph Representations
IfcLLM combines relational and graph representations of IFC models with iterative LLM reasoning to deliver 93.3-100% first-attempt accuracy on natural language queries across three test models.
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IfcLLM: Natural Language Querying of IFC Models through Complementary Relational and Graph Representations
IfcLLM combines relational and graph representations of IFC models with an LLM agent to achieve 93.3-100% first-attempt accuracy on natural language queries across three models and 30 scenarios.
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