Semantic Web Technologies for Intelligent Engineering Applications
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The document discusses the application of semantic web technologies in integrating heterogeneous engineering data for intelligent engineering applications. It emphasizes the development of an engineering knowledge base (EKB) and the AutomationML analyzer to facilitate cross-disciplinary analytics and improve data integration processes. Key results highlight the effective use of linked data technologies to create a 'web of engineering data', enhancing the capabilities of engineering knowledge transfer and problem-solving in the manufacturing industry.
Semantic Web Technologies for Intelligent Engineering Applications
- 1. 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 Mec. Tool Elec. Workflow Analysis SCADA Tool SW Model Mec. Model SW Model Elec.
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- 3. Scientific American, May 2001: 3
- 4. 4 Multi-disciplinary Engineering for Cyber-Physical Production Systems (CPPS)
- 5. Hydro Power Plant Use Case: Example Signal Data Mechanical Engineering Result (OPM Signals) Electrical Engineering Result (EPL Signals) Implicit Links 5
- 6. “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! 6
- 7. Product Hierarchy Process Hierarchy Requirements Resulting Process Plan in Target Production System Chocolate Cake Provider Chocolate Gloss Provider Chocolate Sponge Dough Provider Bitter Chocolate Provider Gloss Provider Chocolate Cake Chocolate Sponge Dough Bitter Chocolate Gloss Chocolate Gloss Chocolate Gloss Provider Chocolate Cake Coating Chocolate Gloss Chocolate Sponge Dough Chocolate Sponge Dough Provider Chocolate Gloss Bitter Chocolate Gloss Bitter Chocolate Provider Gloss Provider 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 '147
- 8. 8 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
- 9. Key Result 1: Engineering Knowledge Base (EKB) 9
- 10. Common Concepts Ontology AutomationML Ontology Key Result 1: Engineering Knowledge Base (EKB) 10
- 11. Problem: Integration of AutomationML Files ①Complex data structure with intricate links between disciplines ②Integration of AutomationML files from different disciplines important ③Limited support for cross-disciplinary analytics ④Limited options for platform independent browsing of AutomationML data AML Files External Sources (e.g., Ecl@ss) Solution? 2 3 4 1 Project Manager Analysis & Monitoring Validation & Defect Detection Browsable Visualization Domain Experts Engineers Quality Manager 11
- 12. 12 AML Files Development External Sources Development (e.g., Ecl@ss) Quality Manager Analysis & Monitoring Validation & Defect Detection Browsable Visualization OWL Transformation RDF Triple store (Predefined) SPARQL Queries Linked Data Visualization Ontology Based Data Integration & Enhancement 1 AML files AML models OWL/RDF external data linked data interface end-user interface 2 Project Manager Domain Experts Engineers AML Analyzer SPARQL query; query results AMLHub - AML Management 3 4 SPARQL Querying Endpoint enhanced OWL/RDF data Feedbacks 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.
- 14. 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.
- 15. 15 Linked Data: May ‘07 > 31 Billion Triples Media Geographic Publications Web 2.0 eGovernment Cross-Domain Life Sciences Sept. ‘11 Source: http://lod-cloud.net WEB OF DATA
- 16. 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 16
- 17. Key Result 3: Towards a “Web of Engineering Data” Enter prise2 LD eCl@ss AutomationML Analyzer (Enterprise1) Festo NXP 17 ABB
- 18. 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” 18