Abstract
Generally, building information modelling (BIM) models contain multiple dimensions of building information, including building design data, construction information, and maintenance-related contents, which are related with different engineering stakeholders. Efficient extraction of BIM data is a necessary and vital step for various data analyses and applications, especially in large-scale BIM projects. In order to extract BIM data, multiple query languages have been developed. However, the use of these query languages for data extraction usually requires that engineers have good programming skills, flexibly master query language(s), and fully understand the Industry Foundation Classes (IFC) express schema or the ontology expression of the IFC schema (ifcOWL). These limitations have virtually increased the difficulties of using query language(s) and raised the requirements on engineers’ essential knowledge reserves in data extraction. In this paper, we develop a simple method for automatic SPARQL (SPARQL Protocol and RDF Query Language) query generation to implement effective data extraction. Based on the users’ data requirements, we match users’ requirements with ifcOWL ontology concepts or instances, search the connected relationships among query keywords based on semantic BIM data, and generate the user-desired SPARQL query. We demonstrate through several case studies that our approach is effective and the generated SPARQL queries are accurate.
Highlights
Building information modelling (BIM) is a general digital building representation and information processing platform, and it has been widely used in the architecture, engineering, and construction (AEC) industry [1,2,3,4]
We only focus on SPARQL generation according to query keywords provided by users/engineers, so natural language processing (NLP) technologies and the engineering applications of data extraction are beyond the scope of this article
The SPARQL query language is widely used in different data extraction or rule compliance checking applications in AEC industry; SPARQL queries are generally programmed manually by experts/engineers
Summary
Building information modelling (BIM) is a general digital building representation and information processing platform, and it has been widely used in the architecture, engineering, and construction (AEC) industry [1,2,3,4]. Semantic web/ontology technologies can facilitate information sharing, integration, and linkage and improve the collaborative ability of different application systems [21] They seem to provide an alternative method for solving issues related to IFC limitations. This raises the bar for SPARQL usage in AEC industry and limits automated data processing to a certain extent To solve this issue, we propose a simple approach to automatically generate the SPARQL queries desired by engineers based on some simple search keywords for BIM data extraction. Utilizing the path query function provided in the Stardog RDF database management system, we search the shortest path that connects all query keywords in a BIM instance and extract the structure of the shortest path to generate the SPARQL query This method can reduce the requirements of programming skills and knowledge reserves of AEC engineers when using.
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