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Semantic Web Technologies for Intelligent Engineering Applications Marta Sabou, Fajar Ekaputra, Olga Kovalenko Estefania Serral, Thomas Moser, Roland Willmann Semantic Representation and Integration of Engineering Knowledge (SRI) Christian Doppler Laboratory Software Engineering Integration for Flexible Automation Systems Tech.Interop. Tool M ec. Tool Elec. Workflow Analysis SCADA Tool SW M odel M ec. M odel SW M odel Elec.

Semantic Web Technologies for Intelligent Engineering Applications

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Page 1: Semantic Web Technologies for  Intelligent Engineering Applications

Semantic Web Technologies for Intelligent Engineering Applications

Marta Sabou, Fajar Ekaputra, Olga KovalenkoEstefania Serral, Thomas Moser, Roland Willmann

Semantic Representation and Integration of Engineering Knowledge (SRI)

Christian Doppler LaboratorySoftware Engineering Integration for Flexible Automation Systems

Tech

. Int

erop

.Tool Mec.

Tool Elec.

Workflow

Analysis

SCADA

Tool SW

Model Mec.

Model SW

Model Elec.

Page 2: Semantic Web Technologies for  Intelligent Engineering Applications

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Page 3: Semantic Web Technologies for  Intelligent Engineering Applications

Scientific American, May 2001:

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Page 4: Semantic Web Technologies for  Intelligent Engineering Applications

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Multi-disciplinary Engineering for Cyber-Physical Production Systems (CPPS)

Page 5: Semantic Web Technologies for  Intelligent Engineering Applications

Hydro Power Plant Use Case: Example Signal Data

Mechanical Engineering Result (OPM Signals)

Electrical Engineering Result (EPL Signals)

Implicit Links

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Page 6: Semantic Web Technologies for  Intelligent Engineering Applications

“All safe software variables should be linked to exactly two sensors”

Software Eng.Mechanical Eng. Electrical Eng.

Intelligent Engineering Applications: Cross-disciplinary Constraint Checking

SELECT ?kks ?signal WHERE { {SELECT ?kks WHERE {

?kks :hasSignal ?signal } GROUP BY ?kks HAVING (COUNT (?signal) >= 2)} ?kks :hasSignal ?signal}}

V_D only linked to one sensor!

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Page 7: Semantic Web Technologies for  Intelligent Engineering Applications

ProductHierarchy

ProcessHierarchyRequirements

ResultingProcess Plan in Target

Production System

ChocolateCakeProvider

ChocolateGlossProvider

ChocolateSponge DoughProvider

BitterChocolateProvider

GlossProvider

ChocolateCake

ChocolateSpongeDough

BitterChocolate Gloss

ChocolateGloss

ChocolateGloss

Provider

ChocolateCake

CoatingChocolate Gloss

ChocolateSpongeDough

ChocolateSponge Dough

Provider

ChocolateGloss

BitterChocolate

Gloss

BitterChocolate

Provider

GlossProvider

provides

requires

Intelligent Engineering Applications:Flexible Matchmaking based Ramp-up Processes

Source: R. Willmann, S. Biffl,  E. Serral. Determining qualified production processes for new product ramp-up using semantic web technologies. i-KNOW '14 7

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Common Concepts provide a common vocabulary to speak about the data in common They link distributed and heterogeneous (local) data models.

Data Integration: Common Concepts

Page 9: Semantic Web Technologies for  Intelligent Engineering Applications

Key Result 1: Engineering Knowledge Base (EKB)

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Common Concepts Ontology

AutomationML Ontology

Key Result 1: Engineering Knowledge Base (EKB)

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Problem: Integration of AutomationML Files

① Complex data structure with intricate links between disciplines② Integration of AutomationML files from different disciplines important ③ Limited support for cross-disciplinary analytics ④ Limited options for platform independent browsing of AutomationML data

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Enables integration, browsing, querying, and analysis of diverse engineering models represented in AutomationML.

Technology transfer:AutomationML Analyzer

Source: M. Sabou, F. J. Ekaputra, O. Kovalenko, S. Biffl (2016). Supporting the Engineering of Cyber-physical Production Systems with the AutomationML Snalyzer. In 1st Int. Ws. on Cyber-Physical Production Systems (CPPS), IEEE.

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Browsable internal links

Different Views on Data

Tool example:AutomationML Analyzer

Page 14: Semantic Web Technologies for  Intelligent Engineering Applications

Key Result 2: Industrial Validation at Siemens A.G.

Exploratory search powered by Semantic Web-based technologies, to bridge data silos of software architecture knowledge

14 Source: J. Musil, F. J. Ekaputra, M. Sabou, T. Ionescu, D. Schall, S. Biffl. Semantic Search for Architectural Knowledge: A Practical Approach. In preparation.

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Linked Data: May ‘07

> 31 Billion Triples

Media

Geographic

Publications

Web 2.0

eGovernment

Cross-Domain

LifeSciences

Sept. ‘11

Source: http://lod-cloud.net

WEB OF DATA

Page 16: Semantic Web Technologies for  Intelligent Engineering Applications

Linked Data-based Intelligent Applications:Manufacturing Industry

NXP Semiconductors integrates data about 20K products

Source: J. Walker. Is Linked Data the future of data integration in the enterprise? 2013 http://blog.nxp.com/slider-main/is-linked-data-the-future-of-data-integration-in-the-enterprise

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Page 17: Semantic Web Technologies for  Intelligent Engineering Applications

Key Result 3:Towards a “Web of Engineering Data”

Enterprise2 LD

eCl@ss AutomationML

Analyzer(Enterprise1)

Festo

NXP

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ABB

Page 18: Semantic Web Technologies for  Intelligent Engineering Applications

Summary

Semantic Web technologies can effectively integrate heterogeneous data sources

The Engineering Knowledge Base (EKB) provides Semantic Web based methods for data integration

AutomationML Analyzer relies on EKB as part of knowledge transfer to company partner

Semantic Web technologies used as basis for semantic search solutions at Siemens A.G.

Use of Linked Data technologies enables move towards a “Web of Engineering Data”

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