During the past two decades, 3D simulation models have gained importance in the development of software solutions that aim to mimic real-world events and phenomena with increasing levels of accuracy and detail. In this context, knowledge representation and processing have recently shown a significant contribution to the simulation modeling domain, where knowledge graphs have been used in different fields to build knowledge representations for multiple purposes. In this paper, we introduce VOWES, a Virtual Outdoor Weather Event Simulator to replicate and measure outdoor weather events in vivid 3D visualizations. We design and implement an integrated knowledge graph (KG) representation for VOWES, by creating two constituent KGs: (i) Weather KG describing weather data and events, and (ii) Simulator KG describing 3D simulation components and properties, and connecting them with the (iii) Semantic Sensor Network (SSN) KG to form an integrated structure serving as the knowledge backbone of the VOWES simulation environment.
| Authors | Noueihed, Hamza and Harb, Heba and Tekli, Joe |
|---|---|
| Year | 2022 |
| Venue | Journal of Supercomputing |
| DOI | 10.1007/s11227-021-04212-6 |
| Source Database | EBSCO (Academic Search Ultimate) |
| Bridge-to-GNN | Category C |
| GNN Architecture | Not yet extracted from PDF |
| Graph Encoding | Not yet extracted from PDF |
| AEC Task | Knowledge Graph Construction (inferred from title) |
| Cohort | Peak Adoption (2022) |
| Implementation Framework | OWL / SPARQL (heuristic — verify) |
|---|---|
| Primary Metric | Accuracy (heuristic — verify) |
| Primary Value | 83% (heuristic — verify) |
| Key Finding | VOWES, a Virtual Outdoor Weather Event Simulator to replicate and measure outdoor weather events in vivid 3D visualizations (heuristic — verify) |
| Quality Assessment | 1/6 · rigor: Low · Baseline: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | RDF / OWL / SPARQL |
| Quality (heuristic, 6-flag) | 0/6 · rigor: Low Code/Data: —Benchmark: —Baseline: —CV/Split: —Ablation: —Reproducible: — |
| Bibliometrically related to other seeds | This paper appeared in the Connected Papers neighbourhood of these corpus seeds: [11] 2025 · Alymani · A graph-based computational tool for retrieving architect… |
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Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026