Abstract
Abstract. Solar energy simulations are used to quantify the potential of the passive use (daylight, solar gains) and the active use (photovoltaics and solar thermal) of solar energy. The simulations can be performed at different scales e.g. buildings, neighbourhoods and cities, with different requirements on the data. For example, for the neighbourhood simulations we need simplified building geometries that can be retrieved from city models, and window information that can be extracted from BIM models (as in many cases window information is missing in city models). In this context, city models and BIM need to be integrated and reconciled. In this paper, we investigate two approaches to integrate and retrieve such information in a case study, where the BIM data is stored in IFC and the city model in CityGML (LOD2). The first approach is to perform a schema matching in an ETL tool, so as to convert and import window information from the IFC file into the CityGML model to create a LOD2-3 building model. We also investigate an alternative avenue, namely a semantic web approach, in which both the BIM and city models are transformed into knowledge graphs (linked data). City models and BIM utilize their respective but interlinked domain ontologies. Particularly, two ontologies are investigated for BIM data, i.e., the ifcOWL ontology and the building topology ontology (BOT). This paper compares different paths of such integrative data retrieval, as well as discloses the gaps mainly with the semantic web approach to further unlock its potential.
Highlights
There is an increasing interest in the use of Building Information Modeling (BIM) data together with geospatial data, i.e. GeoBIM) (Song et al, 2017; Fosu et al, 2015; Ma and Ren, 2017; Liu et al, 2017)
We demonstrate the feasibility of using knowledge graphs for parts of the GeoBIM data integration
We identify that the ontologies have been increasingly mature for these domains, i.e. CityGML and IFC
Summary
There is an increasing interest in the use of Building Information Modeling (BIM) data together with geospatial data, i.e. GeoBIM) (Song et al, 2017; Fosu et al, 2015; Ma and Ren, 2017; Liu et al, 2017). BIM data is converted to geospatial data (often in the form of city models, e.g. CityGML). Such an approach of data integration usually relies on schema matching and conversion methods, which are often implemented using Extract, Transform, Load (ETL) tools. The general aim of this paper is to evaluate the knowledge graph based data integration approach for GeoBIM, from a feasibility perspective. Such a purpose is demonstrated in a case study of solar energy simulations, which entails synthesised information extraction. - Which are the potential benefits/drawbacks of using a knowledge graph based approach for GeoBIM?
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