An Approach of Automatic SPARQL Generation for BIM Data Extraction

Autor: Erling Onstein, Angela Daniela La Rosa, Dongming Guo
Rok vydání: 2020
Předmět:
building information modelling (BIM)
Computer science
020209 energy
0211 other engineering and technologies
ifcOWL
02 engineering and technology
data extraction
Ontology (information science)
Query language
lcsh:Technology
lcsh:Chemistry
021105 building & construction
Industry Foundation Classes
0202 electrical engineering
electronic engineering
information engineering
SPARQL
General Materials Science
lcsh:QH301-705.5
Instrumentation
Protocol (object-oriented programming)
SPARQL generation
computer.programming_language
Fluid Flow and Transfer Processes
Information retrieval
lcsh:T
business.industry
Process Chemistry and Technology
General Engineering
InformationSystems_DATABASEMANAGEMENT
computer.file_format
lcsh:QC1-999
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
Data extraction
Building information modeling
lcsh:TA1-2040
semantic
lcsh:Engineering (General). Civil engineering (General)
business
computer
lcsh:Physics
RDF query language
Zdroj: Applied Sciences, Vol 10, Iss 8794, p 8794 (2020)
Applied Sciences
Volume 10
Issue 24
ISSN: 2076-3417
DOI: 10.3390/app10248794
Popis: 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&rsquo
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&rsquo
data requirements, we match users&rsquo
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.
Databáze: OpenAIRE
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