Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data

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.

傳統網站的一個主要限制是它們的內容無組織且孤立，主要是為了人類消費而創建的。這個限制可以通過使用強大的格式來組織和發布數據來解決，這些格式為網頁的內容添加結構和意義，並將相關數據相互連接。計算機可以更好地“理解”這樣的數據，這對於任務自動化很有用。為軟件代理提供語義（含義）的網站形成了語義網，這是世界廣域網的人工智能擴展。與傳統網絡（“文檔網絡”）相比，語義網包括“數據網絡”，它連接的是“事物”（代表現實世界的人和物體），而不是對計算機無意義的文檔。《精通語義網絡上的結構化數據》解釋了語義網絡背後的實際方面和理論，以及如何使用結構化數據（例如HTML5微數據和JSON-LD）來提高您的網站在下一代搜索引擎結果頁面上的性能並顯示在Google知識面板上。您將學習如何使用資源描述框架（RDF）以機器可解釋的形式表示人類知識的任意領域，這是語義網絡的基石。您將了解如何在專為此目的而建的圖形數據庫（如三元組存儲和四元組存儲）中存儲和操作RDF數據，這些數據在互聯網營銷、社交媒體和數據挖掘方面得到利用，形成了大數據應用，例如Google知識圖、Wikidata或Facebook的社交圖。

隨著網絡服務和應用中不斷增加的用戶期望，語義網絡標準越來越受歡迎。本書將使您熟悉領先的受控詞彙和本體論，並解釋如何表示自己的概念。在學習了聯繫數據、五星部署方案和開放數據概念的原則之後，您將能夠創建和相互連接的五星級開放數據，並將您的RDF圖合併到LOD Cloud中。本書還介紹了生成、存儲、提取和可視化RDF數據的最重要工具，包括但不限於Protégé、TopBraid Composer、Sindice、Apache Marmotta、Callimachus和Tabulator。您將學習在Eclipse和NetBeans等流行的IDE中實現Apache Jena和Sesame，並使用這些API進行快速的語義網絡應用程序開發。《精通語義網絡上的結構化數據》演示了如何表示和連接結構化數據以吸引更廣泛的受眾，鼓勵數據重用，並提供可以自動處理的內容。因此，您的網絡內容將成為Web的下一次革命的重要組成部分。