von Richthofen5, Pieter Herthogs6 1,2,3,5,Singapore-ETH Centre, Future Cities Lab Global Programme, CREATE campus, 1 CREATE Way, #06-01 CREATE Tower, Singapore 138602, 4CARES, Cambridge Centre for Advanced Research and Education in Singapore, Singapore, 5Arup, Berlin 1,2,3{ayda.grisiute|shiying.li|heidi.silvenoinnen}@sec.ethz.ch 4arkadiusz.chadzynski@cares.cam.ac.uk 5Aurel.von-Richthofen@arup.com 5,6{vonrichthofen|herthogs}@arch.ethz.ch Simulation models generate an abundance of rich raw data that remains difficult to access for non-experts. However, such data could be unlocked and utilised with a Semantic City Planning System that improves data accessibility and transparency. This paper describes a process of ontologically representing mobility simulation output data using Semantic Web technologies and storing it in a dynamic geospatial knowledge graph. Our work presents two benefits: 1) formally representing simulation output data increases the accessibility and transparency of urban simulation models, and 2) access to under-utilised rich data unlocks novel cross-domain knowledge explorations and research possibilities. We demonstrate these benefits by means of cross-domain queries related to typical city planning questions.
| Authors | Ayda Grisiute and Heidi Silvennoinen and Shiying Li and Arkadiusz Chadzynski and Aurel Von Richthofen and Pieter Herthogs |
|---|---|
| Year | 2022 |
| Venue | Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe |
| DOI | 10.52842/CONF.ECAADE.2022.2.257 |
| Source Database | Seed (CumInCAD) |
| Bridge-to-GNN | Category Seed |
| GNN Architecture | Knowledge Graph Embedding (inferred from title) |
| Graph Encoding | Knowledge Graph / Ontological Network (inferred from title) |
| AEC Task | Knowledge Graph Construction (inferred from title) |
| Cohort | Peak Adoption (2022) |
| Dataset Size | 257 models (heuristic — verify) |
|---|---|
| Implementation Framework | OWL / SPARQL (heuristic — verify) |
| Primary Metric | Accuracy (heuristic — verify) |
| Key Finding | the results of our first steps towards semantically representing transport simulation outputs using a transportation planning ontology (heuristic — verify) |
| Quality Assessment | 2/6 · rigor: Low · Code/Data: ✓Baseline: ✓ (heuristic — verify) |
| Frameworks / libraries | RDF / OWL / SPARQL |
|---|---|
| Dataset stats found | 257 models |
| 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