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

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Interoperability: sharing information in the wild?

Information loss-

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

Possible strategies to the interoperability challenge

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LINKED DATA APPROACHSEMANTIC WEB TECHNOLOGIES

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links at level of information structure

one generic language with basis in Description Logic (DL)

worldwide usage

+Linking information structures with semantic web technologies

++

=> Semantic web technologies

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Worldwide web of information (‘Linked Open Data cloud’)

+worldwide usage

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Resource Description Framework (RDF)

+one generic language with basis in Description Logic (DL)

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

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+links at level of information structure

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Implementation in an AEC context

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• 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?

Possible strategies to the interoperability challenge

Design thinking process

Information

Information

Information

Information

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Design as an iterative and interactive processInformation systems as environments for design tryouts

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AUTONOMOUS REASONING AGENTS

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Design as an iterative and interactive processAutonomous reasoning agents

Abductive reasoning

Deductive reasoning

Inductive reasoning

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Implementation in JAVA

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Conclusion

Thank you

Information system support in construction industry with semantic web technologies

and/or autonomous reasoning agents

Pieter Pauwels - p.pauwels@uva.nl

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Background knowledge

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Implementation in JAVA

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New design situation

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Implementation in JAVA

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e:possibleModel – e:falseModel – e:counterModel

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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

The abduction