The application of BIM in the building life cycle needs to be continuous. The information collected and accumulated in the early stages should flow to the subsequent phases. However, BIM applications currently focus on collision inspection, compliance inspection, and engineering calculation, few models can be successively used in the following stages. Remodeling is required in the operation and maintenance period, resulting in waste. Meanwhile, some of the information accumulated by BIM might be frequently used in the operation and maintenance stage, while some data are relatively rarely used. The semantic web can help manage building information at all stages. But the generation of a semantic web is mostly manually completed. It is necessary to standardize the repeated semantic description in the model and convert BIM into a standard semantic model for information indexing, reducing the resource consumption of model loading and optimizing the efficiency of the operation and maintenance system.
| Authors | Jingming Li |
|---|---|
| Year | 2023 |
| Venue | Computational Design and Robotic Fabrication |
| DOI | 10.1007/978-981-19-8637-6_17 |
| Source Database | Seed (CumInCAD) |
| Bridge-to-GNN | Category Seed |
| GNN Architecture | Knowledge Graph Embedding (inferred from title) |
| Graph Encoding | BIM Element Topology Graph (inferred from title) |
| AEC Task | Semantic Enrichment (inferred from title) |
| Cohort | Consolidation (2023) |
| Implementation Framework | PyTorch, Grasshopper / Rhino, Revit / Dynamo, OWL / SPARQL (heuristic — verify) |
|---|---|
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 60% (heuristic — verify) |
| Key Finding | method of using Natural Language Processing (NLP) to understand the text and infer the relationship between entities according to the knowledge map (heuristic — verify) |
| Quality Assessment | 1/6 · rigor: Low · Baseline: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | PyTorch · Grasshopper / Rhino · Revit / Dynamo · RDF / OWL / SPARQL · OpenAI / GPT API · Sentence Transformers |
| Reported metrics + values | Accuracy: 60% |
| Quality (heuristic, 6-flag) | 0/6 · rigor: Low Code/Data: —Benchmark: —Baseline: —CV/Split: —Ablation: —Reproducible: — |
Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026