The semantic integration modeling of BIM industry foundations classes and GIS City -geographic markup language are a milestone for many applications that involve both domains of knowledge. In this paper, we propose a system design architecture, and implementation of Extraction, Transformation and Loading (ETL) workflows of BIM and GIS model into RDF graph database model, these workflows were created from functional componen ts and ontological frameworks supporting RDF SPARQL and graph databases Cypher query languages. This paper is about full understanding of whether RDF graph database is suitable for a BIM -GIS integrated information model, and it looks deeper into the assessment of translation workflows and evaluating performance metrics of a BIM-GIS integ rated data model managed in an RDF graph database, the process requires designing and de veloping various pipelines of workflows with semantic tools in order to get the data and its structure into an appropriate format and…
| Authors | Hor, A.-H. and Sohn, G. |
|---|---|
| Year | 2021 |
| Venue | ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences |
| DOI | 10.5194/isprs-annals-viii-4-w2-2021-175-2021 |
| Source Database | EBSCO (Applied Science & Technology) |
| Bridge-to-GNN | Category B |
| GNN Architecture | Not yet extracted from PDF |
| Graph Encoding | BIM Element Topology Graph (inferred from title) |
| AEC Task | Not yet extracted from PDF |
| Cohort | Early Infrastructure (2020–2021) |
| Dataset Size | 19 graphs (heuristic — verify) |
|---|---|
| Implementation Framework | Revit / Dynamo, Neo4j, OWL / SPARQL (heuristic — verify) |
| Primary Metric | Accuracy (heuristic — verify) |
| Quality Assessment | 2/6 · rigor: Low · Code/Data: ✓Baseline: ✓ (heuristic — verify) |
| GNN architectures detected | Knowledge Graph Embedding (TransE/RotatE/DistMult) |
|---|---|
| Frameworks / libraries | Revit / Dynamo · Neo4j / Cypher · RDF / OWL / SPARQL · Apache Jena |
| Benchmark datasets referenced | CityGML |
| Dataset stats found | 19 graphs |
| Quality (heuristic, 6-flag) | 2/6 · 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