An Approach of Automatic SPARQL Generation for BIM Data Extraction

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.

A Semantic Approach for Automated Rule Compliance Checking in Construction Industry

“…Later, the structure of the shortest path will be used to generate SPARQL query. We have discussed an approach for exploring the shortest path in ifcOWL schema in ref [29], so we don't introduce it in this paper. The automatic generated SPARQL query can automatically implement the data extraction from BIM data and produce the compliance checking results.…”

“…Semantic Web and ontology technologies have been widely applied in ACC [1]. Some sub-topics of ACC have been solved, such as ontology-based regulations modeling [26,27], semantic information extraction [27][28][29], semantic mapping [30,31], and compliance checking implementation [4,31].…”

“…We introduced an automatic SPARQL generation approach in ref [29], in which a SPARQL query can be generated according to given query keywords. Utilized these query keywords, a shortest path connected all query keywords will be found in a BIM knowledge base, and then the structure of the shortest path will be used as a query subgraph to generate a SPARQL query.…”

“…However, the SPARQL generation approach in ref [29] did not consider a/some filter(s) of quantitative conditions, we extend our SPARQL generation approach to implement the information extraction through SPARQL query with a/some filter(s) of quantitative conditions. There are built-in filter functions in SPARQL, such as logical judgements (!, &&), math calculations (+, -) and comparison (<, =) etc., so the extracted information that is related with the filter of quantitative conditions (e.g.…”

“…Based on these two characteristics, we can firstly explore the shortest linked structure between a specified property name and its data value, and then keep this shortest linked structure in the exploring the shortest path/structure that connects all mapped keywords in the BIM RDF data. We discussed an approach for generating the shortest path in ref [29], so we don't introduce it here. The structure of shortest path can be used to create a SPARQL query.…”