Semantic Rule-checking for Regulation Compliance Checking: An Overview of

Strategies and Approaches

Pieter Pauwels, Ghent Universitypipauwel.pauwels@ugent.be

Sijie Zhang, Chevron sijie.zhang@chevron.com

CIB W078 conference28 October 2015

INTRODUCTION

[1] European Committee for Standardization (CEN), European Standard EN 12354-3, Building Acoustics — Estimation of Acoustic Performance of Buildings from the Performance of Elements — Part 3: Airborne Sound Insulation Against OutdoorSound, 2000.[2] P. Pauwels, D. Van Deursen, R. Verstraeten, J. De Roo, R. De Meyer, R. Van de Walle, J. Van Campenhout. A semantic rule checking environment for building performance checking. Automation in Construction 20 (2011) 506–518.

Tim Berners-Lee, Semantic Web and Linked Data, http://www.w3.org/2009/Talks/0204-campus-party-tbl/ (slide 14)

Linked Data

Tim Berners-Lee, Semantic Web and Linked Data, http://www.w3.org/2009/Talks/0204-campus-party-tbl/ (slide 14)

First version: Matti Hannus, Hannu Penttilä and Per Silén, Evolution of IT in construction over the last decades, 1987.See http://cic.vtt.fi/hannus/islands/

Tim Berners-Lee, Semantic Web and Linked Data, http://www.w3.org/2009/Talks/0204-campus-party-tbl/ (slide 14)

EXISTING APPROACHESTHE BASICS OF RULE CHECKING

• Pauwels, P., Van Deursen, D., Verstraeten, R., De Roo, J., De Meyer, R., Van de Walle, R. & Van Campenhout, J. (2011). A semantic rule checking environment for building performance checking. Automation in Construction. 20 (5). pp. 506-518.

• Wicaksono, H., Rogalski, S. & Kusnady, E. (2010). Knowledge-based intelligent energy management using building automation system. Proc. of the 2010 IPEC Conference. pp. 1140-1145.

• Wicaksono, H., Dobreva, P., Häfner, P. & Rogalski, S. (2013). Ontology development towards expressive and reasoning-enabled building information model for an intelligent energy management system. Proc. of the 5th International Conference on Knowledge Engineering and Ontology Development.

• Kadolsky, M., Baumgärtel, K. & Scherer, R.J. (2014) An ontology framework for rule-based inspection of eeBIM-systems. Procedia Engineering. 85. pp. 293-301.

• Zhang, S., Boukamp, F. & Teizer, J. (2015). Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA). Automation in Construction. 52. pp. 29-41.

• Zhang, S., Teizer, J., Lee, J.K., Eastman, C.M. & Venugopal, M. (2013). Building information modeling (BIM) and safety: Automatic safety checking of construction models and schedules. Automation in Construction. 29. pp. 183-195.

H. Wicaksono. Rules Integration in OWL BIM for Holistic Energy Management in Operational Phase. 3rd Intl. Workshop on Linked Data in Architecture and Construction. Eindhoven, NL, 2015.

Core components needed for rule checking

1. a schema that defines what kind of information is used by the rule checking process and how it is structured,

2. a set of instances following that schema, and 3. a set of rules (IF-THEN statements) that can

be directly combined with the schema

TBox

ABox

RBox

ADVANTAGES

ABox

TBox RBox

Schema, instances and rules are all described in one and the same language

Þ advantages for a language-driven approach apply, as given by Eastman et al (2009)

1. the possibility to easily retarget an implementation to different source formats (e.g. an alternative ontology: a Revit ontology instead of an IFC ontology);

2. portability across contexts, applications and devices, and 3. the availability of an unlimited representation wealth, including

‘nested conditions’ and ‘branching of alternative contexts’.

Semantic rule checking as a language-driven approach for regulation compliance checking

C.M. Eastman, J. Lee, Y. Jeong, J. Lee, Automatic rule-based checking of building designs, Automation in Construction 18 (2009) 1011–1033.

FlexibilityPortability

Expressiveness

1. Independent from checking systems2. Maintainability3. Conciseness4. Consistency and correctness

Advantages of language-driven approaches

Sibel Macit, M. Emre İlal, Georg Suter, H. Murat Günaydın, A Hybrid Model for Building Code Representation Based on Four-Level and Semantic Modeling Approaches, CIB W78 2015 Conference.

CHALLENGES

?

Ian Horrocks, Bijan Parsia, Peter Patel-Schneider, and James Hendler. Semantic Web Architecture: Stack or Two Towers? Principles and Practice of Semantic Web Reasoning, Volume 3703 of the series Lecture Notes in Computer Science, pp 37-41 (2005).

It’s even more

complicated!!

Expressiveness: SROIQ

W3C OWL Working Group, OWL2 Web Ontology Language Document Overview - W3C Working Draft 27 March 2009, W3C Working Draft, available online: http://www.w3.org/TR/2009/WD-owl2-overview-20090327/. 2009.

ifc:IfcBuilding ifc:IfcSpatialStructureElement

inst:IfcBuilding_1

rdfs:subClassOf

rdf:type

rdf:type

RULE CHECKING STRATEGIES

Zhang, S., Boukamp, F. & Teizer, J. (2015). Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA). Automation in Construction. 52. pp. 29-41.

STRATEGY 1: HARD-CODED RULE CHECKING AFTER QUERYING FOR INFORMATION

WHAT:represent the information that is contained in the rule mentioned earlier as plain RDF data, ideally following an OWL ontology

IMPROVEMENT:Storage of rule knowledge independently from the rule checking applications (portability)

=> Independence from rule checking application (portability)

STRATEGY 2: RULE-CHECKING BY QUERYING

Remarks

• SPARQL queries are fired one by one• They need to be processed by the receiver

(application or end user), so there is no real ‘single output subset graph’ available

• Not easy to write SPARQL queries• Need to maintain SPARQL endpoint• Compliance with a stable ontology needed

STRATEGY 3: SEMANTIC RULE CHECKING WITH DEDICATED RULE LANGUAGES

Remarks• One can combine as many rules as one wishes in one

‘inference run’• The receiver (application or end user) receives the result as a

‘single output subset graph’• The individuals are still in the original namespace (so they have

the same URIs / are identical). The relations, class attributions and potential additional statements can be placed in a separate namespace. But the link to the original is always maintained through the URIs of the individuals.

• Performance can change a lot, depending on the amount of resources and rules that is loaded by the reasoning engine.

• Not easy to write rules• Compliance with a stable ontology needed

DISCUSSING AND CONCLUDING

Best performance?

?

FlexibilityPortability

Expressiveness

Ian Horrocks, Bijan Parsia, Peter Patel-Schneider, and James Hendler. Semantic Web Architecture: Stack or Two Towers? Principles and Practice of Semantic Web Reasoning, Volume 3703 of the series Lecture Notes in Computer Science, pp 37-41 (2005).

Expressiveness: SROIQ

ifc:IfcBuilding ifc:IfcSpatialStructureElement

inst:IfcBuilding_1

rdfs:subClassOf

rdf:type

rdf:type

Flexibility and expressiveness?

[1] Pauwels, P., Van Deursen, D., Verstraeten, R., De Roo, J., De Meyer, R., Van de Walle, R. & Van Campenhout, J. (2011). A semantic rule checking environment for building performance checking. Automation in Construction. 20 (5). pp. 506-518.[2] Sibel Macit, M. Emre İlal, Georg Suter, H. Murat Günaydın, A Hybrid Model for Building Code Representation Based on Four-Level and Semantic Modeling Approaches, CIB W78 2015 Conference. RASE

RASE?

Thank you

Pieter Pauwels, Ghent Universityhttp://users.ugent.be/~pipauwel/

pipauwel.pauwels@ugent.be

Sijie Zhang, Chevron sijie.zhang@chevron.com