Modular production has been recognized as a pivotal approach for enhancing productivity and cost reduction within the industrialized building industry. In the pursuit of further optimization of production processes, the concept of cognitive modular production (CMP) has been proposed, aiming to integrate digital twins (DTs), artificial intelligence (AI), and Internet of Things (IoT) technologies into modular production systems. This fusion would imbue these systems with perception and decision-making capabilities, enabling autonomous operations. However, the efficacy of this approach critically hinges upon the ability to comprehend the production process and its variations, as well as the utilization of IoT and cognitive functionalities. Knowledge graphs (KGs) represent a type of graph database that organizes data into interconnected nodes (entities) and edges (relationships), thereby providing a visual and intuitive representation of intricate systems. This study seeks to investigate the potential fusion of KGs into CMP to bolster decision-making processes on the production line.
| Authors | Jaryani, Soheil and Yitmen, Ibrahim and Sadri, Habib and Alizadehsalehi, Sepehr |
|---|---|
| Year | 2023 |
| Venue | Buildings (2075-5309) |
| DOI | 10.3390/buildings13092306 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category Unclassified |
| GNN Architecture | Knowledge Graph Embedding (heuristic — verify) |
| Graph Encoding | Knowledge Graph / Ontological Network (inferred from title) |
| AEC Task | Knowledge Graph Construction (inferred from title) |
| Cohort | Consolidation (2023) |
| Primary Metric | Accuracy (heuristic — verify) |
|---|---|
| Quality Assessment | 1/6 · rigor: Low · Benchmark: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Benchmark datasets referenced | PubMed |
| Quality (heuristic, 6-flag) | 1/6 · 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