Scripting Intelligence: Web 3.0 Information Gathering and Processing (Expert's Voice in Open Source) Review

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Scripting Intelligence: Web 3.0 Information Gathering and Processing (Expert's Voice in Open Source) ReviewThis four-part book is focused on programming techniques and technologies that in the author's opinion can help next generation web applications handle data more "intelligently". The code samples are implemented in Ruby (and a little bit of Java).
Part One (Chapters 1-3) is basically an introduction to text and natural language processing, sampling tools and techniques for extracting raw text from various document types (e.g., pdf to plain text), classifying a document's subject matter (e.g., is this a document on "Health" or "Politics") or overall sentiment direction (degree of positiveness or negativeness), and recognizing entities such as persons and places in text (e.g., is "Florida" in a given sentence referring to a U.S. state, which is a place entity, or a person whose last name is Florida?).
Part Two (Chapters 4-7) provides tutorials on the Semantic Web, explaining what the RDF subject-predicate-object data format is and how a query language like SPARQL supports inferencing. URLs for publicly available RDF data sets, as well as tools and services useful for exploring them are given.
Part Three (Chapters 8-12) covers topics relating to the use of object-relational mapping (e.g., ActiveRecord and DataMapper used in standalone mode) and search (e.g., Lucene and Sphinx) technologies, publishing relational data as RDF data,and strategies for large-scale data storage involving the use of multiple servers, memcached, CouchDB, Amazon S3, or Amazon EC2.
Part Four (Chapters 13-15) includes a really good tutorial on the use of Hadoop-like Map Reduce facilities for large scale data processing, and ties things together by showing how the knowledge learned from previous chapters can be applied to the development of more substantial web applications.
The author uses many open-source gems (Ruby-centric software library) and tools in this book (most will work fine on Linux, Mac, or Windows, and with Ruby 1.8.x or 1.9, but exceptions are reported clearly), so in many cases, you only need to write a limited amount of code to follow along. If you don't want to download and install the gems, Appendix A provides instructions on how to apply for an Amazon Web Services account to access a ready to use Amazon Machine Image put together by the author for use on a rented Amazon EC2 Server Instance.
Because of the breadth of coverage, each technology can only be discussed to a limited depth, which some readers may find adequate and some may not, depending on a reader's interest on a particular topic, but most should still find this book to be a valuable resource, and that the author explains things well and concisely.Scripting Intelligence: Web 3.0 Information Gathering and Processing (Expert's Voice in Open Source) OverviewIntelligent information management is the key to many Web 2.0 applications, and Semantic Web technologies like the SPARQL query language are helping Web 2.0 evolve into Web 3.0. This book addresses how to use Ruby not only for basic information management tasks, but also how to use it with Semantic Web tools like RDF Schema and SPARQL. This book offers practical advice and example code rather than getting bogged down in the academic and theoretical side of information management.This book offers the reader recipes using Ruby for intelligent information management including 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.

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Building Search Applications: Lucene, LingPipe, and Gate Review

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Building Search Applications: Lucene, LingPipe, and Gate ReviewUnfortunately, there are not too many books written on the subject of Information Retrieval as it relates to Java programming, and thankfully, Mr.Konchady's contribution is an excellent resource. It provides a nice balance between the discussion of the theory of Information Retrieval, and providing concrete examples in Java, using Lucene, LingPipe, and Gate (API's for Information Retrieval used in Java). I personally had only heard of Lucene before coming across this book, and was very thankful to learn of the other two (LingPipe, and Gate) afterwards. The book shows the user how to use the above API's together when building an application, which is a great learning opportunity for the reader, because most tutorials available for Lucene, LingPipe, or Gate that you'll find online show you how to use that particular API only, instead of showing you how to use it in conjunction with others to truly harness the power of Information Retrieval with Natural Language Processing, together. The other very nice thing about this book is that the author also introduces the reader to other tools (such as Nutch, WordNet, etc.) that allows the user to provide advanced functionality, without "re-inventing the wheel". This book is a must read for anyone who is serious about learning to develop applications involving Information Retrieval.Building Search Applications: Lucene, LingPipe, and Gate OverviewLucene, LingPipe, and Gate are popular open source tools to build powerful search applications. Building Search Applications describes functions from Lucene that include indexing, searching, ranking, and spelling correction to build search engines. With this book you will learn to: Extract tokens from text using custom tokenizers and analyzers from Lucene, LingPipe, and Gate. Construct a search engine index with an optional backend database to manage large document collections. Explore the wide range of Lucene queries to search an index, understand the ranking algorithm for a query, and suggest spelling corrections. Find the names of people, places, and other entities in text using LingPipe and Gate. Categorize documents by topic using classifiers and build groups of self-organized documents using clustering algorithms from LingPipe. Create a Web crawler to scan the Web, Intranet, or desktop using Nutch. Track the sentiment of articles published on the Web with LingPipe.

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