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Information system support in construction industry
with semantic web technologies
and/or autonomous reasoning agents

Pieter Pauwels, University of Amsterdam
Ronald De Meyer, Ghent University

Jan Van Campenhout, Ghent University
Interoperability: sharing information in the wild?

Information loss

2
Possible strategies to the interoperability challenge

•
•
•
•
•

Sharing information in the wild
Manual remodelling of information
Negotiation with top-level ontologies
Central information structure
Linked data approach

3
LINKED DATA APPROACH
SEMANTIC WEB TECHNOLOGIES
4
Linking information structures
with semantic web technologies

+
+
+

links at level of information structure
one generic language
with basis in Description Logic (DL)
worldwide usage

=> Semantic web technologies
5
Worldwide web of information (‘Linked Open Data cloud’)

+

worldwide usage

6
Resource Description Framework (RDF)

+

one generic language
with basis in Description Logic (DL)

7
+

links at level of information structure

CultureSampo: Finnish Culture on the
Semantic Webwww.kulttuurisampo.fi

8
Implementation in an AEC context

9
Possible strategies to the interoperability challenge

•
•
•
•
•

Sharing information in the wild
Manual remodelling of information
Negotiation with top-level ontologies
Central information structure
Linked data approach

=> How to get the right information at the right
time?
10
Design thinking process

Information

Information

Information

Information
Design as an iterative and interactive process
Information systems as environments for design tryouts

12
AUTONOMOUS REASONING
AGENTS
13
Design as an iterative and interactive process
Autonomous reasoning agents

Abductive reasoning

Inductive reasoning
Deductive reasoning

14
Implementation in JAVA

15
Conclusion

16
Thank you
Information system support in construction industry
with semantic web technologies
and/or autonomous reasoning agents
Pieter Pauwels - p.pauwels@uva.nl
Background knowledge

18
Implementation in JAVA

19
New design situation

20
Implementation in JAVA

21
e:possibleModel – e:falseModel – e:counterModel

22
The abduction

100 relevant previous experiences
100 models generated for each query
(‘insulation’ – ‘windows’ – ‘heater’)
> belief in ‘insulation’: 0.84
> belief in ‘windows’: 0.58
> belief in ‘heater’: 0.54
23

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