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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
Web-based information systems, such as search engines, news
portals, electronic markets and community sites, provide access to
information originating from numerous information providers. The
quality of provided information varies as information providers
have different levels of knowledge and different intentions. Users
of web-based systems are therefore confronted with the increasingly
difficult task to select high quality information from the vast
amount of Web-accessible information. How can information systems
support users to distinguish high quality from low quality
information? Which filtering mechanisms can be applied? How can
filtering decisions be explained to the user? The book gives an
overview about information quality assessment in context of
web-based systems. Afterwards, a quality-driven information
filtering framework is developed. The framework allows information
consumers to apply a wide range of different filtering policies. In
order to facilitate the information consumers' understanding of
filtering decisions, the framework generates explanations why
information satisfies a specific policy. The book targets Web
developers who need to handle information quality problems within
their applications as well as researchers working on the topic.
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