Leslie F. Sikos - Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data
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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 sites 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 Facebooks 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, Protg, 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 D ...
Leslie F. Sikos: author's other books
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Sam Featherston (editor)
Panos Alexopoulos
Vishal Jain (editor)
Calvary Gaëlle
Juanzi Li Guilin Qi Dongyan Zhao Wolfgang Nejdl
Blace Ryan
Allemang Dean Hendler James
Bob DuCharme
Charles Ruhl
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Leslie F. Sikos, Ph.D. 2015
Leslie F. Sikos Mastering Structured Data on the Semantic Web 10.1007/978-1-4842-1049-9_11. Introduction to the Semantic Web
Leslie F. Sikos 1
(1)
SA, Australia
The content of conventional web sites is human-readable only, which is unsuitable for automatic processing and inefficient when searching for related information. Web datasets can be considered as isolated data silos that are not linked to each other. 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 Semantic Web
While binary files often contain machine-readable metadata, such as the shutter speed in a JPEG image) 
. 
. Figure 1-1.
The evolution of the Web [5]
Caution
The word semantic is used on the Web in other contexts as well. For example, in HTML5 there are semantic (in other words, meaningful) structuring elements, but this expression refers to the meaning of elements. In this context, the word semantic contrasts the meaning of elements, such as that of section (a thematic grouping), with the generic elements of older HTML versions, such as the meaningless div . The semantics of markup elements should not be confused with the semantics (in other words, machine-processability) of metadata annotations and web ontologies used on the Semantic Web. The latter can provide far more sophisticated data than the meaning of a markup element.
In contrast to the conventional Web (the Web of documents), the Semantic Web includes the Web of Data [6], which connects things (representing real-world humans and objects) rather than documents meaningless to computers. The machine-readable datasets of the Semantic Web are used in a variety of web services [7], such as search engines, data integration, resource discovery and classification, cataloging, intelligent software agents, content rating, and intellectual property right descriptions [8], museum portals [9], community sites [10], podcasting [11], Big Data processing [12], business process modeling [13], and medical research. On the Semantic Web, data can be retrieved from seemingly unrelated fields automatically, in order to combine them, find relations, and make discoveries [14].
Structured Data
Conventional web sites rely on markup languages for document structure, style sheets for appearance, and scripts for behavior, but the content is human-readable only. When searching for Jaguar on the Web, for example, traditional search engine algorithms cannot always tell the difference between the British luxury car and the South American predator (Figure ). 
Figure 1-2.
Traditional web search algorithms rely heavily on context and file names
A typical web page contains structuring elements, formatted text, and some even multimedia objects. By default, the headings, texts, links, and other web site components created by the web designer are meaningless to computers. While browsers can display web documents based on the markup, only the human mind can interpret the meaning of information, so there is a huge gap between what computers and humans understand (see Figure elements), the data is not structured or linked to related data, and human-readable words of conventional web page paragraphs are not associated with any particular software syntax or structure. Without context, the information provided by web sites can be ambiguous to search engines. 
Figure 1-3.
Traditional web site contents are meaningless to computers
The concept of machine-readable data is not new, and it is not limited to the Web. Think of the credit cards or barcodes, both of which contain human-readable and machine-readable data (Figure ). One person or product, however, has more than one identifier, which can cause ambiguity. 
Figure 1-4.
Human-readable and machine-readable data
Even the well-formed XML documents, which follow rigorous syntax rules, have serious limitations when it comes to machine-processability. For instance, if an XML entity is defined between and , what does SLR stand for? It can refer to a single-lens reflex camera, a self-loading rifle, a service-level report, system-level requirements, the Sri Lankan rupee, and so on.
Contents can be made machine-processable and unambiguous by adding organized (structured) data to the web sites, as markup annotations or as dedicated external metadata files, and linking them to other, related structured datasets. Among other benefits, structured data files support a much wider range of tasks than conventional web sites and are far more efficient to process. Structured data formats have been used for decades in computing, especially in Access and SQL relational databases, where queries can be performed to retrieve information efficiently. Because there are standards for direct mapping of relational databases to core Semantic Web technologies, databases that were publicly unavailable can now be shared on the Semantic Web [15]. Commercial database software packages powered by Semantic Web standards are also available on the market (5Store, AllegroGraph, BigData, Oracle, OWLIM, Talis Platform, Virtuoso, and so on) [16].
In contrast to relational databases, most data on the Web is stored in (X)HTML documents that contain unstructured data to be rendered in web browsers as formatted text, images, and multimedia objects. Publishing unstructured data works satisfactorily for general purposes; however, a large amount of data stored in, or associated with, traditional web documents cannot be processed this way. The data used to describe social connections between people is a good example, which should include the relationship type and multiple relationship directions inexpressible with the hyperlinks of the conventional Web [17].
The real benefit of semantic annotations is that humans can browse the conventional web documents, while Semantic Web crawlers can process the machine-readable annotations to classify data entities, discover logical links between entities, build indices, and create navigation and search pages.
Semantic Web Components
Structured data processing relies on technologies that provide a formal description of concepts, terms, and relationships within a knowledge domain (field of interest, discipline). Knowledge Representation and Reasoning is the field of Artificial Intelligence (AI) used to represent information in a machine-readable form that computer systems can utilize to solve complex tasks. Taxonomies or controlled vocabularies are structured collections of terms that can be used as metadata element values. For example, an events vocabulary can be used to describe concerts, lectures, and festivals in a machine-readable format, while an organization vocabulary is suitable for publishing machine-readable metadata about a school, a corporation, or a club. The controlled vocabularies are parts of conceptual data schemas (data models) that map concepts and their relationships.
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