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Über dieses Buch

This is the first book to explore how Semantic Web technologies (SWTs) can be used to create intelligent engineering applications (IEAs). Technology-specific chapters reflect the state of the art in relevant SWTs and offer guidelines on how they can be applied in multi-disciplinary engineering settings characteristic of engineering production systems. In addition, a selection of case studies from various engineering domains demonstrate how SWTs can be used to create IEAs that enable, for example, defect detection or constraint checking.

Part I “Background and Requirements of Industrie 4.0 for Semantic Web Solutions” provides the background information needed to understand the book and addresses questions concerning the semantic challenges and requirements of Industrie 4.0, and which key SWT capabilities may be suitable for implementing engineering applications. In turn, Part II “Semantic Web-Enabled Data Integration in Multi-Disciplinary Engineering” focuses on how SWTs can be used for data integration in heterogeneous, multi-disciplinary engineering settings typically encountered in the creation of flexible production systems. Part III “Creating Intelligent Applications for Multi-Disciplinary Engineering” demonstrates how the integrated engineering data can be used to support the creation of IEAs, while Part IV “Related and Emerging Trends in the Use of Semantic Web in Engineering” presents an overview of the broader spectrum of approaches that make use of SWTs to support engineering settings. A final chapter then rounds out the book with an assessment of the strengths, weaknesses and compatibilities of SWTs and an outlook on future opportunities for applying SWTs to create IEAs in flexible industrial production systems.

This book seeks to build a bridge between two communities: industrial production on one hand and Semantic Web on the other. Accordingly, stakeholders from both communities should find this book useful in their work. Semantic Web researchers will gain a better understanding of the challenges and requirements of the industrial production domain, offering them guidance in the development of new technologies and solutions for this important application area. In turn, engineers and managers from engineering domains will arrive at a firmer grasp of the benefits and limitations of using SWTs, helping them to select and adopt appropriate SWTs more effectively. In addition, researchers and students interested in industrial production-related issues will gain valuable insights into how and to what extent SWTs can help to address those issues.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
This chapter introduces the context and aims of this book. In addition, it provides a detailed description of industrial production systems including their life cycle, stakeholders, and data integration challenges. It also includes an analysis of the types of intelligent engineering applications that are needed to support flexible production in line with the views of current smart manufacturing initiatives, in particular Industrie 4.0.
Stefan Biffl, Marta Sabou

Background and Requirements of Industrie 4.0 for Semantic Web Solutions

Frontmatter

Chapter 2. Multi-Disciplinary Engineering for Industrie 4.0: Semantic Challenges and Needs

Abstract
This chapter introduces key concepts of the Industrie 4.0 vision, focusing on variability issues in traditional and cyber-physical production systems (CPPS) and their engineering processes . Four usage scenarios illustrate key challenges of system engineers and managers in the transition from traditional to CPPS engineering environments. We derive needs for semantic support from the usage scenarios as a foundation for evaluating solution approaches and discuss Semantic Web capabilities to address the identified multidisciplinary engineering needs. We compare the strengths and limitations of Semantic Web capabilities to alternative solution approaches in practice. Semantic Web technologies seem to be a very good match for addressing the aspects of heterogeneity in engineering due to their capability to integrate data intelligently and flexibly on a large scale. Engineers and managers from engineering domains can use the scenarios to select and adopt appropriate Semantic Web solutions in their own settings.
Stefan Biffl, Arndt Lüder, Dietmar Winkler

Chapter 3. An Introduction to Semantic Web Technologies

Abstract
The process of engineering cyber-physical production systems (CPPS) relies on the collaborative work of multiple and diverse teams of engineers who need to exchange and synchronize data created by their domain-specific tools. Building applications that support the CPPS engineering process requires technologies that enable integrating and making sense of heterogeneous datasets produced by various engineering disciplines. Are Semantic Web technologies suitable to fulfill this task? This chapter aims to answer this question by introducing the reader to key Semantic Web concepts in general and core Semantic Web technologies (SWT) in particular, including ontologies, Semantic Web knowledge representation languages, and Linked Data. The chapter concludes this technology overview with a specific focus on core SWT capabilities that qualify them for intelligent engineering applications. These capabilities include (i) formal and flexible semantic modeling, (ii) intelligent, web-scale knowledge integration, (iii) browsing and exploration of distributed data sets, (iv) knowledge quality assurance and (v) knowledge reuse.
Marta Sabou

Semantic Web Enabled Data Integration in Multi-disciplinary Engineering

Frontmatter

Chapter 4. The Engineering Knowledge Base Approach

Abstract
Systems and software engineering projects depend on the cooperation of experts from heterogeneous engineering domains using tools that were not designed to cooperate seamlessly. Current semantic engineering tool and data integration is often ad hoc and fragile, thereby making the evolution of tools and the reuse of integration solutions across projects unnecessarily inefficient and risky. This chapter describes the engineering knowledge base (EKB) framework for engineering environment integration in multidisciplinary engineering projects. The EKB stores explicit engineering knowledge to support access to and management of engineering models across tools and disciplines. The following Chaps. 57 discuss individual aspects of the EKB framework, which provides (1) data integration based on mappings between local and domain-level engineering concepts; (2) transformations between local engineering concepts; and (3) advanced applications built on these foundations, e.g., end-to-end analyses. As a result, experts from different organizations may use their well-known tools and data models and can access data from other tools in their syntax. Typical applications enabled by implementations of this framework are discussed in Chaps. 9 and 10.
Thomas Moser

Chapter 5. Semantic Modelling and Acquisition of Engineering Knowledge

Abstract
Ontologies are key Semantic Web technologies (SWTs) that provide means to formally and explicitly represent domain knowledge in terms of key domain concepts and their relations. Therefore, the creation of intelligent engineering applications (IEAs) that rely on SWTs depends on the creation of a suitable ontology that semantically models engineering knowledge and the representation of engineering data in terms of this ontology (i.e., through a knowledge acquisition process). The tasks of semantic modelling and acquisition of engineering knowledge are, however, complex tasks that rely on specialized skills provided by a knowledge engineer and can therefore be daunting for those SWT adopters that do not possess this skill set. This chapter aims to support these SWT adopters by summing up essential knowledge for creating and populating ontologies including: ontology engineering methodologies and methods for assessing the quality of the created ontologies. The chapter provides examples of concrete engineering ontologies, and classifies these engineering ontologies in a framework based on the Product-Process-Resource abstraction. The chapter also contains examples of best practices for modelling common situations in the engineering domain using ontology design patterns, and gives an overview of the current tools that engineers ca use to lift engineering data stored in legacy formats (such as, spreadsheets, XML files, and databases, etc.) to a semantic representation.
Marta Sabou, Olga Kovalenko, Petr Novák

Chapter 6. Semantic Matching of Engineering Data Structures

Abstract
An important element of implementing a data integration solution in multi-disciplinary engineering settings, consists in identifying and defining relations between the different engineering data models and data sets that need to be integrated. The ontology matching field investigates methods and tools for discovering relations between semantic data sources and representing them. In this chapter, we look at ontology matching issues in the context of integrating engineering knowledge. We first discuss what types of relations typically occur between engineering objects in multi-disciplinary engineering environments taking a use case in the power plant engineering domain as a running example. We then overview available technologies for mappings definition between ontologies, focusing on those currently most widely used in practice and briefly discuss their capabilities for mapping representation and potential processing. Finally, we illustrate how mappings in the sample project in power plant engineering domain can be generated from the definitions in the Expressive and Declarative Ontology Alignment Language (EDOAL).
Olga Kovalenko, Jérôme Euzenat

Chapter 7. Knowledge Change Management and Analysis in Engineering

Abstract
Knowledge is changing rapidly within the engineering process of Cyber-Physical Production Systems (CPPS) characterized by the collaborative work of engineers from diverse engineering disciplines. Such rapid changes lead to the need for management and analysis of knowledge changes in order to preserve knowledge consistency. Knowledge change management and analysis (KCMA) in Multidisciplinary Engineering (MDEng) environments is a challenging task since it involves heterogeneous, versioned, and linked data in a mission-critical fashion, where failure to provide correct data could be costly. Although, there are several available solutions for addressing general issues of KCMA, from fields as diverse as Model-Based Engineering (model co-evolution), Databases (database schema evolution), and Semantic Web Technology (ontology versioning), solving KCMA in engineering remains a challenging task. In this chapter, we investigate issues related to KCMA in MDEng environments. We provide a definition of this task and some of its challenges and we overview technologies that can be potentially used for solving KCMA tasks from the three research fields mentioned above. We then define a technology agnostic solution approach inspired by the Ontology-Based Information Integration approach from Semantic Web research as a first step toward a complete KCMA solution and provide an indication of how this solution concept could be implemented using state of the art Semantic Web technologies.
Fajar Juang Ekaputra

Intelligent Applications for Multi-disciplinary Engineering

Frontmatter

Chapter 8. Semantic Data Integration: Tools and Architectures

Abstract
This chapter is focused on the technical aspects of semantic data integration that provides solutions for bridging semantic gaps between common project-level concepts and the local tool concepts as identified in the Engineering Knowledge Base (EKB). Based on the elicitation of use case requirements from automation systems engineering, the chapter identifies required capabilities an EKB software architecture has to consider. The chapter describes four EKB software architecture variants and their components, and discusses identified drawbacks and advantages regarding the utilization of ontologies. A benchmark is defined to evaluate the efficiency of the EKB software architecture variants in the context of selected quality attributes, like performance and scalability. Main results suggest that architectures relying on a relational database still outperform traditional ontology storages while NoSQL databases outperforms for query execution.
Richard Mordinyi, Estefania Serral, Fajar Juang Ekaputra

Chapter 9. Product Ramp-up for Semiconductor Manufacturing Automated Recommendation of Control System Setup

Abstract
Predictable and fast production launch of new products (product ramp-up) is a crucial success factor in the production industry in general, and for the production of integrated circuits (ICs) in particular. During the ramp-up phase of the product there is, inter alia, the need for product-specific configuration of a wide range of software systems that control the production process in a fully automated manner. This collection of software systems is sourced from several vendors and has therefore to be configured in different ways. Moreover, configuration has to be orchestrated along the whole production process, in accordance with the needs of the new product. This is a complicated, error-prone, and time-consuming task for product engineers, process engineers, and application engineers. The approach described in this chapter avoids such efforts and risks through a semiautomated generation of configurations of software systems. It uses a knowledge base which provides a unified configuration schema across all involved software systems. The approach applies automated reasoning of new configuration content based on the knowledge about new products’ characteristics and knowledge about the existing production environment. The knowledge base is described by ontology models and based on Semantic Web technologies. The described approach is the basis for IT professionals of IC factories and of factories with comparable IT infrastructure to standardize the configuration of software systems being in charge for production control and process control. It contributes to accelerate the launch of new products and to make their ramp-up phase more deterministic.
Roland Willmann, Wolfgang Kastner

Chapter 10. Ontology-Based Simulation Design and Integration

Abstract
Strict requirements on the quality of industrial plant operation together with environmental limits and the pursuit of decreasing energy consumption bring more complexity in automation systems. Simulations and models of industrial processes can be utilized in all the phases of an automation system’s life cycle and they can be used for process design as well as for optimal plant operation. Present methods of design and integration of simulations tasks are inefficient and error-prone because almost all pieces of information and knowledge are handled manually. In this chapter, we describe a simulation framework where all configurations, simulation tasks, and scenarios are obtained from a common knowledge base. The knowledge base is implemented utilizing an ontology for defining a data model to represent real-world concepts, different engineering knowledge as well as descriptions and relations to other domains. Ontologies allow the capturing of structural changes in simulations and evolving simulation scenarios more easily than using standard relational databases. Natively ontologies are used to represent the knowledge shared between different projects and systems. The simulation framework provides tools for efficient integration of data and simulations by exploiting the advantages of formalized knowledge. Two processes utilizing Semantic Web technologies within the simulation framework are presented at the end of this chapter.
Radek Šindelář, Petr Novák

Related and Emerging Trends in the Use of Semantic Web in Engineering

Frontmatter

Chapter 11. Semantic Web Solutions in Engineering

Abstract
The Industrie 4.0 vision highlights the need for more flexible and adaptable production systems. This requires making the process of engineering production systems faster and intends to lead to higher quality, but also more complex plants. A key issue in improving engineering processes in this direction is providing mechanisms that can efficiently and intelligently handle large-scale and heterogeneous engineering data sets thus shortening engineering processes while ensuring a higher quality of the engineered system, for example, by enabling improved cross-disciplinary defect detection mechanisms. Semantic Web technologies (SWTs) have been widely used for the development of a range of Intelligent Engineering Applications (IEAs) that exhibit an intelligent behavior when processing large and heterogeneous data sets. This chapter identifies key technical tasks performed by IEAs, provides example IEAs and discusses the connection between Semantic Web capabilities and IEA tasks.
Marta Sabou, Olga Kovalenko, Fajar Juang Ekaputra, Stefan Biffl

Chapter 12. Semantic Web Solutions in the Automotive Industry

Abstract
This chapter describes how we employed ontologies to solve and optimize different design tasks at an automotive company. We first introduce five core engineering ontologies that provides the formal grounding for the described use cases. We used these ontology to represent engineering systems , and to perform change propagation and consistency checking of the design models. The first use case presents an approach that helps engineers derive an optimized design starting from a system specification, whose parameters are refined in iterative design steps. This use case will demonstrate how we represented requirements and their iterative refinements using ontologies, and how we used a constraint solver in conjunction with an ontology to derive an optimized design with respect to different criteria. The second use case comes from the collaborative development process of an automatic gearbox , in which distributed engineering teams developed different models (i.e., geometric and functional) of the same product in a parallel way. We used ontologies to represent the different models of the same engineering product, and used formal mappings to define the correspondences between the different models. We could then use reasoning to check the consistency of the two models with each other, and to propagate changes from one model to the other.
Tania Tudorache, Luna Alani

Chapter 13. Leveraging Semantic Web Technologies for Consistency Management in Multi-viewpoint Systems Engineering

Abstract
Systems modeling is an important ingredient for engineering complex systems in potentially heterogeneous environments. One way to deal with the increasing complexity of systems is to offer several dedicated viewpoints on the system model for different stakeholders, thus providing means for system engineers to focus on particular aspects of the environment. This allows them to solve engineering tasks more efficiently, although keeping those multiple viewpoints consistent with each other (e.g., in dynamic multiuser scenarios) is not trivial. In the present chapter, we elaborate how Semantic Web technologies (SWT) may be utilized to deal with such challenges when models are represented as RDF graphs. In particular, we discuss current developments regarding a W3C Recommendation for describing structural constraints over RDF graphs called Shapes Constraint Language (SHACL) which we subsequently exploit for defining intermodel constraints to ensure consistency between different viewpoints represented as RDF graphs. Based on a running example, we illustrate how SHACL is used to define correspondences (i.e., mappings) between different RDF graphs and subsequently how those correspondences can be validated during modeling time.
Simon Steyskal, Manuel Wimmer

Chapter 14. Applications of Semantic Web Technologies for the Engineering of Automated Production Systems—Three Use Cases

Abstract
The increasing necessity to adapt automated production systems rapidly to changing requirements requires a better support for planning, developing and operating automated production systems. One means to improve the engineering of these complex systems is the use of models , thereby abstracting the view on the system and providing a common base to improve understanding and communication between engineers. However, in order for any engineering project to be successful, it is essential to keep the created engineering models consistent. We envision the use of Semantic Web Technologies for such consistency checks in the domain of Model-Based Engineering . In this chapter, we show how Semantic Web Technologies can support consistency checking for the engineering process in the automated production systems domain through three distinct use cases : In a first use case, we illustrate the combination of a Systems Modeling Language-based notation with Web Ontology Language (OWL) to ensure compatibility between mechatronic modules after a module change. A second use case demonstrates the application of OWL with the SPARQL Query Language to ensure consistency during model-based requirements and test case design for automated production systems. In a third use case, it is shown how the combination of the Resource Description Framework (RDF) and the SPARQL Query Language can be used to identify inconsistencies between interdisciplinary engineering models of automated production systems. We conclude with opportunities of applying Semantic Web Technologies to support the engineering of automated production systems and derive the research questions that need to be answered in future work.
Stefan Feldmann, Konstantin Kernschmidt, Birgit Vogel-Heuser

Chapter 15. Conclusions and Outlook

Abstract
This chapter summarizes and reflects on the material presented in this book. In particular, the chapter aims to conclude on the relation between Industrie 4.0 needs and Semantic Web technologies (SWTs) based on the various intelligent engineering applications (IEAs) reported in Parts III and IV of the book. Concretely, this chapter seeks answers to the following questions: which Industrie 4.0 scenarios are addressed by engineering applications described in this book? Which are the most and least used capabilities of SWTs? Which limitations of SWTs seem important and which alternative technologies can be used to compensate for those limitations? What is the technological blueprint of an IEA and what SWTs are typically needed to instantiate this blueprint? This analysis and reflections lead to an outlook on the future application of SWTs for building IEAs to address Industrie 4.0 tasks.
Marta Sabou, Stefan Biffl

Backmatter

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