This paper introduces a dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data are connected, portable, discoverable, and queryable via a uniform interface. The knowledge graph includes the notions of a “base world” that describes the real world and that is maintained by agents that incorporate real-time data, and of “parallel worlds” that support the intelligent exploration of alternative designs without affecting the base world.
| Authors | Akroyd, J.; Mosbach, S.; Bhave, A.; Kraft, M. |
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| Year | 2021 |
| Venue | Data-Centric Engineering 2, e14 |
| DOI | 10.1017/dce.2021.10 |
| Source Database | Backtracking |
| Bridge-to-GNN | Category C |
| 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 | Early Infrastructure (2020–2021) |
| Implementation Framework | OWL / SPARQL (heuristic — verify) |
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| Primary Metric | Accuracy (heuristic — verify) |
| Key Finding | dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin (heuristic — verify) |
| Quality Assessment | 1/6 · rigor: Low · Code/Data: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | RDF / OWL / SPARQL · Apache Jena |
| Benchmark datasets referenced | CityGML |
| Code repositories found in text | https://github.com/SDG-InterfaceOntology/ |
| Quality (heuristic, 6-flag) | 2/6 · rigor: Low Code/Data: ✓Benchmark: ✓Baseline: —CV/Split: —Ablation: —Reproducible: — |
| Seed of .bib export → verified neighbours | This paper was queried as a Connected Papers seed (41 entries scanned). In-corpus neighbours verified by the bibliometric ranking: [58] 2024 · Nabrotzky · Structural Analysis Models Using Graph Neural Networks [62] 2020 · Wang · Knowledge Graph for Dunhuang Cultural Heritage |
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Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026