Direct Translation Versus Semantic Reinterpretation in IFC-to-CityGML 3.0 Conversion: Assessing Traceability and Quantity Preservation Through RDF Knowledge Graphs

Converting building information models in the Industry Foundation Classes (IFC) into CityGML 3.0 brings building interiors into urban digital twins and knowledge graphs, yet converted models are often assessed only for schema validity. Converters either translate IFC elements one by one (direct translation) or rebuild thematic surfaces, rooms and levels of detail through interpretive decisions (semantic reinterpretation). This study makes those decisions explicit and measurable. We implemented a reinterpretation converter with switchable decisions, an area-preserving polygon reconstruction, and a converter-agnostic GML-to-RDF transformation based on the unmodified LIRIS CityGML 3.0 ontology, GeoSPARQL and PROV-O. On a railway-station model (IFC2x3, 23,861 products), we compared our converter with an open-source direct-translation converter against reference quantities from IFC geometry. For the 3649 objects not simplified in the default run, both converters preserved surface area equally well (95.4% and 95.2% within tolerance); prism simplification multiplied the summed volume of the simplified objects by 7.2. Every IFC product was either converted one-to-one or excluded by a recorded decision, whereas the direct translation’s output lacked 728 products. With curtain walls excluded and simplification applied, the reinterpreted GML was 26 times smaller and answered queries on room area in the standard vocabulary and on wall surfaces by standard class with auxiliary storey and area properties, which the direct translation could not. Controlled runs varying the two decisions independently attributed the volume loss and most of the size reduction to simplification; with it off, reinterpretation matched direct translation on all 23,133 common objects (98.7% versus 98.6% within tolerance).