Dieter Fensel

This book describes methods and tools that empower information providers to build and maintain knowledge graphs, including those for manual, semi-automatic, and automatic construction; implementation; and validation and verification of semantic annotations and their integration into knowledge graphs. It also presents lifecycle-based approaches for semi-automatic and automatic curation of these graphs, such as approaches for assessment, error correction, and enrichment of knowledge graphs with other static and dynamic resources. Chapter 1 defines knowledge graphs, focusing on the impact of various approaches rather than mathematical precision. Chapter 2 details how knowledge graphs are built, implemented, maintained, and deployed. Chapter 3 then introduces relevant application layers that can be built on top of such knowledge graphs, and explains how inference can be used to define views on such graphs, making it a useful resource for open and service-oriented dialog systems. Chapter 4 discusses applications of knowledge graph technologies for e-tourism and use cases for other verticals. Lastly, Chapter 5 provides a summary and sketches directions for future work. The additional appendix introduces an abstract syntax and semantics for domain specifications that are used to adapt schema.org to specific domains and tasks. To illustrate the practical use of the approaches presented, the book discusses several pilots with a focus on conversational interfaces, describing how to exploit knowledge graphs for e-marketing and e-commerce. It is intended for advanced professionals and researchers requiring a brief introduction to knowledge graphs and their implementation.

This textbook introduces the theoretical foundations of technologies essential for knowledge graphs. It also covers practical examples, applications and tools. Knowledge graphs are the most recent answer to the challenge of providing explicit knowledge about entities and their relationships by potentially integrating billions of facts from heterogeneous sources. The book is structured in four parts. For a start, Part I lays down the overall context of knowledge graph technology. Part II “Knowledge Representation” then provides a deep understanding of semantics as the technical core of knowledge graph technology. Semantics is covered from different perspectives, such as conceptual, epistemological and logical. Next, Part III “Knowledge Modelling” focuses on the building process of knowledge graphs. The book focuses on the phases of knowledge generation, knowledge hosting, knowledge assessment, knowledge cleaning, knowledge enrichment, and knowledge deployment to cover a complete life cycle for this process. Finally, Part IV (simply called “Applications”) presents various application areas in detail with concrete application examples as well as an outlook on additional trends that will emphasize the need for knowledge graphs even stronger. This textbook is intended for graduate courses covering knowledge graphs. Besides students in knowledge graph, Semantic Web, database, or information retrieval classes, also advanced software developers for Web applications or tools for Web data management will learn about the foundations and appropriate methods.

A paradigm shift is taking place in computer science: one generation ago, we learned to abstract from hardware to software, now we are abstracting from software to serviceware implemented through service-oriented computing. Yet ensuring interoperability in open, heterogeneous, and dynamically changing environments, such as the Internet, remains a major challenge for actual machine-to-machine integration. Usually significant problems in aligning data, processes, and protocols appear as soon as a specific piece of functionality is used within a different application context. The Semantic Web Services (SWS) approach is about describing services with metadata on the basis of domain ontologies as a means to enable their automatic location, execution, combination, and use. Fensel and his coauthors provide a comprehensive overview of SWS in line with actual industrial practice. They introduce the main sociotechnological components that ground the SWS vision (like Web Science, Service Science, and service-oriented architectures) and several approaches that realize it, e.g. the Web Service Modeling Framework, OWL-S, and RESTful services. The real-world relevance is emphasized through a series of case studies from large-scale R&D projects and a business-oriented proposition from the SWS technology provider Seekda. Each chapter of the book is structured according to a predefined template, covering both theoretical and practical aspects, and including walk-through examples and hands-on exercises. Additional learning material is available on the book website www.swsbook.org. With its additional features, the book is ideally suited as the basis for courses or self-study in this field, and it may also serve as a reference for researchers looking for a state-of-the-art overview of formalisms, methods, tools, and applications related to SWS.

Within the framework of so-called second generation expert systems [62] knowledge modeling is one of the most important aspects. On the one hand, knowledge acquisition is no longer seen as a knowledge transfer process, rather it is now considered as model construction process which is typically a cyclic and error prone process. On the other hand, the distinction between knowledge and symbol level descriptions [166] resulted in various proposals for conceptual knowledge models describing knowledge in an implementation independent way. One of the most prominent examples of such a conceptual model is the KADS model of expertise which is characterized by its clear distinction of different know ledge types and by the usage of specific modeling primitives to describe these different knowledge types [185]. The semi­ formal KADS expertise model entails all the advantages and disadvantages which have been identified for semi-formal system models e.g. in the software engineering community.

A paradigm shift is taking place in computer science: one generation ago, we learned to abstract from hardware to software, now we are abstracting from software to serviceware implemented through service-oriented computing. Yet ensuring interoperability in open, heterogeneous, and dynamically changing environments, such as the Internet, remains a major challenge for actual machine-to-machine integration. Usually significant problems in aligning data, processes, and protocols appear as soon as a specific piece of functionality is used within a different application context. The Semantic Web Services (SWS) approach is about describing services with metadata on the basis of domain ontologies as a means to enable their automatic location, execution, combination, and use. Fensel and his coauthors provide a comprehensive overview of SWS in line with actual industrial practice. They introduce the main sociotechnological components that ground the SWS vision (like Web Science, Service Science, and service-oriented architectures) and several approaches that realize it, e.g. the Web Service Modeling Framework, OWL-S, and RESTful services. The real-world relevance is emphasized through a series of case studies from large-scale R&D projects and a business-oriented proposition from the SWS technology provider Seekda. Each chapter of the book is structured according to a predefined template, covering both theoretical and practical aspects, and including walk-through examples and hands-on exercises. Additional learning material is available on the book website www.swsbook.org. With its additional features, the book is ideally suited as the basis for courses or self-study in this field, and it may also serve as a reference for researchers looking for a state-of-the-art overview of formalisms, methods, tools, and applications related to SWS.

Ontologies have been developed and investigated for some time in artificial intelligence to facilitate knowledge sharing and reuse. More recently, the notion of ontologies has attracted attention from fields such as databases, intelligent information integration, cooperative information systems, information retrieval, electronic commerce, enterprise application integration, and knowledge management. This broadened interest in ontologies is based on the feature that they provide a machine-processable semantics of information sources that can be communicated among agents as well as between software artifacts and humans. This feature makes ontologies the backbone technology of the next web generation, i.e., the Semantic Web. Ontologies are currently applied in areas such as knowledge management in large company-wide networks and call centers, and in B2C, B2G, and B2B electronic commerce. In a nutshell, ontologies enable effective and efficient access to heterogeneous and distributed information sources. Given the increasing amount of information available online, this kind of support is becoming more important day by day. The author systematically introduces the notion of ontologies to the non-expert reader and demonstrates in detail how to apply this conceptual framework for improved intranet retrieval of corporate information and knowledge and for enhanced Internet-based electronic commerce. He also describes ontology languages (XML, RDF, and OWL) and ontology tools, and the application of ontologies. In addition to structural improvements, the second edition covers recent developments relating to the Semantic Web, and emerging web-based standard languages. ___________

Service-oriented computing has become one of the predominant factors in current IT research and development. Web services seem to be the middleware solution of the future for highly interoperable distributed software solutions. In parallel, research on the Semantic Web provides the results required to exploit distributed machine-processable data. To combine these two research lines into industrial-strength applications, a number of research projects have been set up by organizations like W3C and the EU. Dieter Fensel and his coauthors deliver a profound introduction into one of the most promising approaches – the Web Service Modeling Ontology (WSMO). After a brief presentation of the underlying basic technologies and standards of the World Wide Web, the Semantic Web, and Web Services, they detail all the elements of WSMO from basic concepts to possible applications in e-commerce, e-government and e-banking, and they also describe its relation to other approaches like OWL-S or WSDL-S. While many of the related technologies and standards are still under development, this book already offers both a broad conceptual introduction and lots of pointers to future application scenarios for researchers in academia and industry as well as for developers of distributed Web applications.

Service-oriented computing has become one of the predominant factors in current IT research and development. Web services seem to be the middleware solution of the future for highly interoperable distributed software solutions. In parallel, research on the Semantic Web provides the results required to exploit distributed machine-processable data. To combine these two research lines into industrial-strength applications, a number of research projects have been set up by organizations like W3C and the EU. Dieter Fensel and his coauthors deliver a profound introduction into one of the most promising approaches – the Web Service Modeling Ontology (WSMO). After a brief presentation of the underlying basic technologies and standards of the World Wide Web, the Semantic Web, and Web Services, they detail all the elements of WSMO from basic concepts to possible applications in e-commerce, e-government and e-banking, and they also describe its relation to other approaches like OWL-S or WSDL-S. While many of the related technologies and standards are still under development, this book already offers both a broad conceptual introduction and lots of pointers to future application scenarios for researchers in academia and industry as well as for developers of distributed Web applications.

Ontologies have been developed and investigated for some time in artificial intelligence to facilitate knowledge sharing and reuse. More recently, the notion of ontologies has attracted attention from fields such as databases, intelligent information integration, cooperative information systems, information retrieval, electronic commerce, enterprise application integration, and knowledge management. This broadened interest in ontologies is based on the feature that they provide a machine-processable semantics of information sources that can be communicated among agents as well as between software artifacts and humans. This feature makes ontologies the backbone technology of the next web generation, i.e., the Semantic Web. Ontologies are currently applied in areas such as knowledge management in large company-wide networks and call centers, and in B2C, B2G, and B2B electronic commerce. In a nutshell, ontologies enable effective and efficient access to heterogeneous and distributed information sources. Given the increasing amount of information available online, this kind of support is becoming more important day by day. The author systematically introduces the notion of ontologies to the non-expert reader and demonstrates in detail how to apply this conceptual framework for improved intranet retrieval of corporate information and knowledge and for enhanced Internet-based electronic commerce. He also describes ontology languages (XML, RDF, and OWL) and ontology tools, and the application of ontologies. In addition to structural improvements, the second edition covers recent developments relating to the Semantic Web, and emerging web-based standard languages. ___________

Researchers in Artificial Intelligence have traditionally been classified into two categories: the “neaties” and the “scruffies”. According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it “an art”, thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).

Within the framework of so-called second generation expert systems [62] knowledge modeling is one of the most important aspects. On the one hand, knowledge acquisition is no longer seen as a knowledge transfer process, rather it is now considered as model construction process which is typically a cyclic and error prone process. On the other hand, the distinction between knowledge and symbol level descriptions [166] resulted in various proposals for conceptual knowledge models describing knowledge in an implementation independent way. One of the most prominent examples of such a conceptual model is the KADS model of expertise which is characterized by its clear distinction of different know ledge types and by the usage of specific modeling primitives to describe these different knowledge types [185]. The semi­ formal KADS expertise model entails all the advantages and disadvantages which have been identified for semi-formal system models e.g. in the software engineering community.