The Web of Data
The Web of Data

Author: Aidan Hogan

Publisher: Springer Nature

Published: 2020-09-09

Total Pages: 689

ISBN-13: 303051580X

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This book’s main goals are to bring together in a concise way all the methodologies, standards and recommendations related to Data, Queries, Links, Semantics, Validation and other issues concerning machine-readable data on the Web, to describe them in detail, to provide examples of their use, and to discuss how they contribute to – and how they have been used thus far on – the “Web of Data”. As the content of the Web becomes increasingly machine readable, increasingly complex tasks can be automated, yielding more and more powerful Web applications that are capable of discovering, cross-referencing, filtering, and organizing data from numerous websites in a matter of seconds. The book is divided into nine chapters, the first of which introduces the topic by discussing the shortcomings of the current Web and illustrating the need for a Web of Data. Next, “Web of Data” provides an overview of the fundamental concepts involved, and discusses some current use-cases on the Web where such concepts are already being employed. “Resource Description Framework (RDF)” describes the graph-structured data model proposed by the Semantic Web community as a common data model for the Web. The chapter on “RDF Schema (RDFS) and Semantics” presents a lightweight ontology language used to define an initial semantics for terms used in RDF graphs. In turn, the chapter “Web Ontology Language (OWL)” elaborates on a more expressive ontology language built upon RDFS that offers much more powerful ontological features. In “SPARQL Query Language” a language for querying and updating RDF graphs is described, with examples of the features it supports, supplemented by a detailed definition of its semantics. “Shape Constraints and Expressions (SHACL/ShEx)” introduces two languages for describing the expected structure of – and expressing constraints on – RDF graphs for the purposes of validation. “Linked Data” discusses the principles and best practices proposed by the Linked Data community for publishing interlinked (RDF) data on the Web, and how these techniques have been adopted. The final chapter highlights open problems and rounds out the coverage with a more general discussion on the future of the Web of Data. The book is intended for students, researchers and advanced practitioners interested in learning more about the Web of Data, and about closely related topics such as the Semantic Web, Knowledge Graphs, Linked Data, Graph Databases, Ontologies, etc. Offering a range of accessible examples and exercises, it can be used as a textbook for students and other newcomers to the field. It can also serve as a reference handbook for researchers and developers, as it offers up-to-date details on key standards (RDF, RDFS, OWL, SPARQL, SHACL, ShEx, RDB2RDF, LDP), along with formal definitions and references to further literature. The associated website webofdatabook.org offers a wealth of complementary material, including solutions to the exercises, slides for classes, raw data for examples, and a section for comments and questions.

Data on the Web
Data on the Web

Author: Serge Abiteboul

Publisher: Morgan Kaufmann

Published: 2000

Total Pages: 280

ISBN-13: 9781558606227

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Data model. Queries. Types. Sysems. A syntax for data. XML.. Query languages. Query languages for XML. Interpretation and advanced features. Typing semistructured data. Query processing. The lore system. Strudel. Database products supporting XML. Bibliography. Index. About the authors.

Linked Data
Linked Data

Author: Tom Heath

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 122

ISBN-13: 303179432X

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The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study. Table of Contents: List of Figures / Introduction / Principles of Linked Data / The Web of Data / Linked Data Design Considerations / Recipes for Publishing Linked Data / Consuming Linked Data / Summary and Outlook

Linked Data
Linked Data

Author: Luke Ruth

Publisher: Simon and Schuster

Published: 2013-12-30

Total Pages: 402

ISBN-13: 163835216X

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Summary Linked Data presents the Linked Data model in plain, jargon-free language to Web developers. Avoiding the overly academic terminology of the Semantic Web, this new book presents practical techniques, using everyday tools like JavaScript and Python. About this Book The current Web is mostly a collection of linked documents useful for human consumption. The evolving Web includes data collections that may be identified and linked so that they can be consumed by automated processes. The W3C approach to this is Linked Data and it is already used by Google, Facebook, IBM, Oracle, and government agencies worldwide. Linked Data presents practical techniques for using Linked Data on the Web via familiar tools like JavaScript and Python. You'll work step-by-step through examples of increasing complexity as you explore foundational concepts such as HTTP URIs, the Resource Description Framework (RDF), and the SPARQL query language. Then you'll use various Linked Data document formats to create powerful Web applications and mashups. Written to be immediately useful to Web developers, this book requires no previous exposure to Linked Data or Semantic Web technologies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. What's Inside Finding and consuming Linked Data Using Linked Data in your applications Building Linked Data applications using standard Web techniques About the Authors David Wood is co-chair of the W3C's RDF Working Group. Marsha Zaidman served as CS chair at University of Mary Washington. Luke Ruth is a Linked Data developer on the Callimachus Project. Michael Hausenblas led the Linked Data Research Centre. Table of Contents PART 1 THE LINKED DATA WEB Introducing Linked Data RDF: the data model for Linked Consuming Linked Data PART 2 TAMING LINKED DATA Creating Linked Data with SPARQL—querying the Linked PART 3 LINKED DATA IN THE WILD Enhancing results from search RDF database fundamentals Datasets PART 4 PULLING IT ALL TOGETHER Callimachus: a Linked Data Publishing Linked Data—a recap The evolving Web

Reasoning Techniques for the Web of Data
Reasoning Techniques for the Web of Data

Author: A. Hogan

Publisher: IOS Press

Published: 2014-04-09

Total Pages: 344

ISBN-13: 1614993831

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Linked Data publishing has brought about a novel “Web of Data”: a wealth of diverse, interlinked, structured data published on the Web. These Linked Datasets are described using the Semantic Web standards and are openly available to all, produced by governments, businesses, communities and academia alike. However, the heterogeneity of such data – in terms of how resources are described and identified – poses major challenges to potential consumers. Herein, we examine use cases for pragmatic, lightweight reasoning techniques that leverage Web vocabularies (described in RDFS and OWL) to better integrate large scale, diverse, Linked Data corpora. We take a test corpus of 1.1 billion RDF statements collected from 4 million RDF Web documents and analyse the use of RDFS and OWL therein. We then detail and evaluate scalable and distributed techniques for applying rule-based materialisation to translate data between different vocabularies, and to resolve coreferent resources that talk about the same thing. We show how such techniques can be made robust in the face of noisy and often impudent Web data. We also examine a use case for incorporating a PagerRank-style algorithm to rank the trustworthiness of facts produced by reasoning, subsequently using those ranks to fix formal contradictions in the data. All of our methods are validated against our real world, large scale, open domain, Linked Data evaluation corpus.

Web Data Mining
Web Data Mining

Author: Bing Liu

Publisher: Springer Science & Business Media

Published: 2011-06-25

Total Pages: 637

ISBN-13: 3642194605

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Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Data Mining the Web
Data Mining the Web

Author: Zdravko Markov

Publisher: John Wiley & Sons

Published: 2007-04-06

Total Pages: 236

ISBN-13: 0470108088

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This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).

Web Data Management
Web Data Management

Author: Serge Abiteboul

Publisher: Cambridge University Press

Published: 2011-11-28

Total Pages: 451

ISBN-13: 113950505X

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The Internet and World Wide Web have revolutionized access to information. Users now store information across multiple platforms from personal computers to smartphones and websites. As a consequence, data management concepts, methods and techniques are increasingly focused on distribution concerns. Now that information largely resides in the network, so do the tools that process this information. This book explains the foundations of XML with a focus on data distribution. It covers the many facets of distributed data management on the Web, such as description logics, that are already emerging in today's data integration applications and herald tomorrow's semantic Web. It also introduces the machinery used to manipulate the unprecedented amount of data collected on the Web. Several 'Putting into Practice' chapters describe detailed practical applications of the technologies and techniques. The book will serve as an introduction to the new, global, information systems for Web professionals and master's level courses.

Mastering Structured Data on the Semantic Web
Mastering Structured Data on the Semantic Web

Author: Leslie Sikos

Publisher: Apress

Published: 2015-07-11

Total Pages: 244

ISBN-13: 1484210492

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A major limitation of conventional web sites is their unorganized and isolated contents, which is created mainly for human consumption. This limitation can be addressed by organizing and publishing data, using powerful formats that add structure and meaning to the content of web pages and link related data to one another. Computers can "understand" such data better, which can be useful for task automation. The web sites that provide semantics (meaning) to software agents form the Semantic Web, the Artificial Intelligence extension of the World Wide Web. In contrast to the conventional Web (the "Web of Documents"), the Semantic Web includes the "Web of Data", which connects "things" (representing real-world humans and objects) rather than documents meaningless to computers. Mastering Structured Data on the Semantic Web explains the practical aspects and the theory behind the Semantic Web and how structured data, such as HTML5 Microdata and JSON-LD, can be used to improve your site’s performance on next-generation Search Engine Result Pages and be displayed on Google Knowledge Panels. You will learn how to represent arbitrary fields of human knowledge in a machine-interpretable form using the Resource Description Framework (RDF), the cornerstone of the Semantic Web. You will see how to store and manipulate RDF data in purpose-built graph databases such as triplestores and quadstores, that are exploited in Internet marketing, social media, and data mining, in the form of Big Data applications such as the Google Knowledge Graph, Wikidata, or Facebook’s Social Graph. With the constantly increasing user expectations in web services and applications, Semantic Web standards gain more popularity. This book will familiarize you with the leading controlled vocabularies and ontologies and explain how to represent your own concepts. After learning the principles of Linked Data, the five-star deployment scheme, and the Open Data concept, you will be able to create and interlink five-star Linked Open Data, and merge your RDF graphs to the LOD Cloud. The book also covers the most important tools for generating, storing, extracting, and visualizing RDF data, including, but not limited to, Protégé, TopBraid Composer, Sindice, Apache Marmotta, Callimachus, and Tabulator. You will learn to implement Apache Jena and Sesame in popular IDEs such as Eclipse and NetBeans, and use these APIs for rapid Semantic Web application development. Mastering Structured Data on the Semantic Web demonstrates how to represent and connect structured data to reach a wider audience, encourage data reuse, and provide content that can be automatically processed with full certainty. As a result, your web contents will be integral parts of the next revolution of the Web.

Exploiting Semantic Web Knowledge Graphs in Data Mining
Exploiting Semantic Web Knowledge Graphs in Data Mining

Author: P. Ristoski

Publisher: IOS Press

Published: 2019-06-28

Total Pages: 246

ISBN-13: 1614999813

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Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.