Integrating IFC and CityGML Model at Schema Level by Using Linguistic and Text Mining Techniques

There is a growing interest in the interoperability between building information models (BIM) and geographic information systems (GIS). BIM can provide rich information for the generation of a GIS model, while the analysis tools in GIS can be used by BIM applications, such as the management of material supply chains and site selection. A comprehensive integration of the BIM and GIS is critical for interoperability between the two domains. However, many integration methods including manual inspection of complex and heterogeneous model files are time-consuming and have weak generalisation capability. This paper proposes a new linguistic-based semantic mapping method that uses the letter trigram-based word hashing method (WHSMM) to construct the feature vector, and then maps the Industry Foundation Classes (IFC) and City Geography Markup Language (CityGML) schemas through text mining techniques at schema level. The semantic mapping precision and recall obtained using WHSMM are compared with those of another linguistic-based method. The mapping result shows that performance of WHSMM is superior to that of another method in most cases. Therefore, the mapping candidates of WHSMM are used to transform the IFC datasets into CityGML models. The transformation results show that most of mapping candidates of the WHSMM can be used to practical data integration. The semantic mapping method presented in this paper can help map the IFC and CityGML schemas by effectively reducing the search space of the manual mapping method.