# LLM-assisted Graph-RAG Information Extraction from IFC Data

arXiv: https://arxiv.org/abs/2504.16813 · HTML: https://mlsanity.com/paper/2504.16813/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [Language is All a Graph Needs](https://mlsanity.com/paper/2308.07134/) (2023)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://mlsanity.com/paper/2210.02627/) (2022)
- [Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2408.04187/) (2024)
- [Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting](https://mlsanity.com/paper/2311.13314/) (2023)
- [BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval](https://mlsanity.com/paper/2304.09333/) (2023)
- [A natural-language-based approach to intelligent data retrieval and representation for cloud BIM](https://mlsanity.com/paper/2411.09951/) (2024)

## Built on by (most cited first, 4 of 4)

- [Multi-Agent GraphRAG: A Text-to-Cypher Framework for Labeled Property Graphs](https://mlsanity.com/paper/2511.08274/) (2025)
- [An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance](https://mlsanity.com/paper/2508.16602/) (2025)
- [IfcLLM: Natural Language Querying of IFC Models through Complementary Relational and Graph Representations](https://mlsanity.com/paper/2605.13236/) (2026)
- [Reinforcement learning to improve large language model-based automated code compliance systems](https://mlsanity.com/paper/2606.22402/) (2026)

## Abstract (arXiv)

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.