Scripting Intelligence: Web 3.0 Information, Gathering and Processing

While Web 2.0 was about data, Web 3.0 is about knowledge and information. Scripting Intelligence: Web 3.0 Information Gathering and Processing offers the reader Ruby scripts for intelligent information management in a Web 3.0 environmentincluding information extraction from text, using Semantic Web [...]
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While Web 2.0 was about data, Web 3.0 is about knowledge and information. Scripting Intelligence: Web 3.0 Information Gathering and Processing offers the reader Ruby scripts for intelligent information management in a Web 3.0 environmentincluding information extraction from text, using Semantic Web technologies, information gathering (relational database metadata, web scraping, Wikipedia, Freebase), combining information from multiple sources, and strategies for publishing processed information. This book will be a valuable tool for anyone needing to gather, process, and publish web or database information across the modern web environment.Text processing recipes, including speech tagging and automatic summarization Gathering, visualizing, and publishing information from the Semantic Web Information gathering from traditional sources such as relational databases and web sites What you'll learn Gather and process information within the Web 3.0 environment. See the flexibility of scripting with Ruby to gather and process information. Extract text from various document formats. Work with the Resource Description Framework(RDF) data model and SPARQL query language, the foundations of the Semantic Web. Use GraphViz for data visualization. Extract information from relational databases and web sites. Who this book is for Anyone needing to gather and display information available in electronic formats Programmers needing to tag, summarize, or publish information Ruby programmers and computer enthusiasts interested in seeing what Ruby can do with information management and Semantic Web tools Academic researchers needing to extract and organize information in a more automated way Table of Contents Parsing Common Document Types Cleaning, Segmenting, and Spell-Checking Text Natural Language Processing Using RDF and RDFS Data Formats Delving Into RDF Data Stores Performing SPARQL Queries and Understanding Reasoning Implementing SPARQL Endpoint Web Portals Working with Relational Databases Supporting Indexing and Search Using Web Scraping to Create Semantic Relations Taking Advantage of Linked Data Implementing Strategies for Large-Scale Data Storage Creating Web Mashups Performing Large-Scale Data Processing Building Information Web Portals