A performance benchmark over semantic rule checking approaches in construction industry

P. Pauwels, T. de Farias, C. Zhang, A. Roxin, J. Beetz, J. De Roo, C. Nicolle

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)
1 Downloads (Pure)

Abstract

As more and more architectural design and construction data is represented using the Resource Description Framework (RDF) data model, it makes sense to take advantage of the logical basis of RDF and implement a semantic rule checking process as it is currently not available in the architectural design and construction industry. The argument for such a semantic rule checking process has been made a number of times by now. However, there are a number of strategies and approaches that can be followed regarding the realization of such a rule checking process, even when limiting to the use of semantic web technologies. In this article, we compare three reference rule checking approaches that have been reported earlier for semantic rule checking in the domain of architecture, engineering and construction (AEC). Each of these approaches has its advantages and disadvantages. A criterion that is tremendously important to allow adoption and uptake of such semantic rule checking approaches, is performance. Hence, this article provides an overview of our collaborative test results in order to obtain a performance benchmark for these approaches. In addition to the benchmark, a documentation of the actual rule checking approaches is discussed. Furthermore, we give an indication of the main features and decisions that impact performance for each of these three approaches, so that system developers in the construction industry can make an informed choice when deciding for one of the documented rule checking approaches.

Original languageEnglish
Pages (from-to)68-88
Number of pages21
JournalAdvanced Engineering Informatics
Volume33
DOIs
Publication statusPublished - 1 Aug 2017

Fingerprint

Construction industry
Semantics
Architectural design
Semantic Web
Data structures

Keywords

  • Benchmark
  • ifcOWL
  • Linked data
  • Reasoning
  • Rule checking
  • Semantic web

Cite this

Pauwels, P. ; de Farias, T. ; Zhang, C. ; Roxin, A. ; Beetz, J. ; De Roo, J. ; Nicolle, C. / A performance benchmark over semantic rule checking approaches in construction industry. In: Advanced Engineering Informatics. 2017 ; Vol. 33. pp. 68-88.
@article{58dc7662a9524a4081f921643d779491,
title = "A performance benchmark over semantic rule checking approaches in construction industry",
abstract = "As more and more architectural design and construction data is represented using the Resource Description Framework (RDF) data model, it makes sense to take advantage of the logical basis of RDF and implement a semantic rule checking process as it is currently not available in the architectural design and construction industry. The argument for such a semantic rule checking process has been made a number of times by now. However, there are a number of strategies and approaches that can be followed regarding the realization of such a rule checking process, even when limiting to the use of semantic web technologies. In this article, we compare three reference rule checking approaches that have been reported earlier for semantic rule checking in the domain of architecture, engineering and construction (AEC). Each of these approaches has its advantages and disadvantages. A criterion that is tremendously important to allow adoption and uptake of such semantic rule checking approaches, is performance. Hence, this article provides an overview of our collaborative test results in order to obtain a performance benchmark for these approaches. In addition to the benchmark, a documentation of the actual rule checking approaches is discussed. Furthermore, we give an indication of the main features and decisions that impact performance for each of these three approaches, so that system developers in the construction industry can make an informed choice when deciding for one of the documented rule checking approaches.",
keywords = "Benchmark, ifcOWL, Linked data, Reasoning, Rule checking, Semantic web",
author = "P. Pauwels and {de Farias}, T. and C. Zhang and A. Roxin and J. Beetz and {De Roo}, J. and C. Nicolle",
year = "2017",
month = "8",
day = "1",
doi = "10.1016/j.aei.2017.05.001",
language = "English",
volume = "33",
pages = "68--88",
journal = "Advanced Engineering Informatics",
issn = "1474-0346",
publisher = "Elsevier",

}

A performance benchmark over semantic rule checking approaches in construction industry. / Pauwels, P.; de Farias, T.; Zhang, C.; Roxin, A.; Beetz, J.; De Roo, J.; Nicolle, C.

In: Advanced Engineering Informatics, Vol. 33, 01.08.2017, p. 68-88.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A performance benchmark over semantic rule checking approaches in construction industry

AU - Pauwels, P.

AU - de Farias, T.

AU - Zhang, C.

AU - Roxin, A.

AU - Beetz, J.

AU - De Roo, J.

AU - Nicolle, C.

PY - 2017/8/1

Y1 - 2017/8/1

N2 - As more and more architectural design and construction data is represented using the Resource Description Framework (RDF) data model, it makes sense to take advantage of the logical basis of RDF and implement a semantic rule checking process as it is currently not available in the architectural design and construction industry. The argument for such a semantic rule checking process has been made a number of times by now. However, there are a number of strategies and approaches that can be followed regarding the realization of such a rule checking process, even when limiting to the use of semantic web technologies. In this article, we compare three reference rule checking approaches that have been reported earlier for semantic rule checking in the domain of architecture, engineering and construction (AEC). Each of these approaches has its advantages and disadvantages. A criterion that is tremendously important to allow adoption and uptake of such semantic rule checking approaches, is performance. Hence, this article provides an overview of our collaborative test results in order to obtain a performance benchmark for these approaches. In addition to the benchmark, a documentation of the actual rule checking approaches is discussed. Furthermore, we give an indication of the main features and decisions that impact performance for each of these three approaches, so that system developers in the construction industry can make an informed choice when deciding for one of the documented rule checking approaches.

AB - As more and more architectural design and construction data is represented using the Resource Description Framework (RDF) data model, it makes sense to take advantage of the logical basis of RDF and implement a semantic rule checking process as it is currently not available in the architectural design and construction industry. The argument for such a semantic rule checking process has been made a number of times by now. However, there are a number of strategies and approaches that can be followed regarding the realization of such a rule checking process, even when limiting to the use of semantic web technologies. In this article, we compare three reference rule checking approaches that have been reported earlier for semantic rule checking in the domain of architecture, engineering and construction (AEC). Each of these approaches has its advantages and disadvantages. A criterion that is tremendously important to allow adoption and uptake of such semantic rule checking approaches, is performance. Hence, this article provides an overview of our collaborative test results in order to obtain a performance benchmark for these approaches. In addition to the benchmark, a documentation of the actual rule checking approaches is discussed. Furthermore, we give an indication of the main features and decisions that impact performance for each of these three approaches, so that system developers in the construction industry can make an informed choice when deciding for one of the documented rule checking approaches.

KW - Benchmark

KW - ifcOWL

KW - Linked data

KW - Reasoning

KW - Rule checking

KW - Semantic web

UR - http://www.scopus.com/inward/record.url?scp=85019966891&partnerID=8YFLogxK

U2 - 10.1016/j.aei.2017.05.001

DO - 10.1016/j.aei.2017.05.001

M3 - Article

AN - SCOPUS:85019966891

VL - 33

SP - 68

EP - 88

JO - Advanced Engineering Informatics

JF - Advanced Engineering Informatics

SN - 1474-0346

ER -