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[7] Semantic 3D City Database — An enabler for a dynamic geospatial knowledge graph

Knowledge Graph / Semantic Infrastructure Energy Estimation & Digital Twins 2021

Paper Description extracted from PDF · abstract · 144 words

This paper presents a system of autonomous intelligent software agents, based on a cognitive architecture, capable of automated instantiation, visualisation and analysis of multifaceted City Information Models in dynamic geospatial knowledge graphs. Design of JPS Agent Framework and Routed Knowledge Graph Access components was required in order to provide backbone infrastructure for an intelligent agent system as well as technology agnostic knowledge graph access enabling automation of multi-domain data interoperability. Development of CityImportAgent, CityExportAgent and DistanceAgent showcased intelligent automation capabilities of the Cities Knowledge Graph. The agents successfully created a semantic model of Berlin in LOD 2, compliant with CityGML 2.0 standard and consisting of 419 909 661 triples described using OntoCityGML. The system of agents also visualised and analysed the model by autonomously tracking interactions with a web interface as well as enriched the model by adding new information to the knowledge graph.

AuthorsChadzynski, A.; Krdzavac, N.; Farazi, F.; Lim, M. Q.; Li, S.; Grisiute, A.; Herthogs, P.; von Richthofen, A.; Cairns, S.; Kraft, M.
Year2021
VenueEnergy and AI 6, 100106
DOI10.1016/j.egyai.2021.100106
Source DatabaseBacktracking
Bridge-to-GNNCategory C
GNN ArchitectureKnowledge Graph Embedding (inferred from title)
Graph EncodingKnowledge Graph / Ontological Network (inferred from title)
AEC TaskKnowledge Graph Construction (inferred from title)
CohortEarly Infrastructure (2020–2021)

Extracted Methodology heuristic — verify before citing

Dataset Size274 buildings (heuristic — verify)
Implementation FrameworkOWL / SPARQL (heuristic — verify)
Key Hyperparameters0 hidden units (heuristic — verify)
Primary MetricAccuracy (heuristic — verify)
Key Findingone of the ways of achieving such goal (heuristic — verify)
Quality Assessment2/6 · rigor: Low ·
Code/Data: ✓Reproducible: ✓ (heuristic — verify)

Detected Technical Stack auto-detected from PDF · regex catalogue match

GNN architectures detectedKnowledge Graph Embedding (TransE/RotatE/DistMult)
Frameworks / librariesRDF / OWL / SPARQL
Benchmark datasets referencedCityGML
Code repositories found in texthttps://github.com/OloOcki/ · https://github.com/blazegraph/database/ · https://github.com/cambridge-cares/TheWorldAvatar/ · https://github.com/cambridgecares/TheWorldAvatar/tree/develo · https://github.com/blazegraph/
Hyperparameters (regex-detected)hidden_units=0
Dataset stats found274 buildings
Quality (heuristic, 6-flag)2/6 · rigor: Low
Code/Data: ✓Benchmark: ✓Baseline: —CV/Split: —Ablation: —Reproducible: —

Lineage Families

energy-digital-twin bim-gis-integration

Enabled By / Precursors

[6]

Extended By

[20] [37]

Infrastructure Dependencies

BIM Knowledge Graph (RDF/OWL/SPARQL) (auto-suggested) RDF / OWL / SPARQL (detected from PDF)

Research Frontier Tags

knowledge-graph

Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026