Logo - springer
Slogan - springer

Engineering - Computational Intelligence and Complexity | Learning Structure and Schemas from Documents

Learning Structure and Schemas from Documents

Biba, Marenglen, Xhafa, Fatos (Eds.)

2011, XVIII, 442p. 98 illus..

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$159.00

(net) price for USA

ISBN 978-3-642-22913-8

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$209.00

(net) price for USA

ISBN 978-3-642-22912-1

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • Presents State-Of-The-Art Methods for Structure Learning and Schema
  • Inference Case Studies and Best Practices from Real Large Scale Digital Libraries, Repositories and Corpora
  • Written by Leading Experts in the Field

The rapidly growing volume of available digital documents of various formats and the possibility to access these through Internet-based technologies, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Due to the extremely large volumes of documents and to their unstructured form, most of the research efforts in this direction are dedicated to automatically infer structure and schemas that can help to better organize huge collections of documents and data.

 

This book covers the latest advances in structure inference in heterogeneous collections of documents and data. The book brings a comprehensive view of the state-of-the-art in the area, presents some lessons learned and identifies new research issues, challenges and opportunities for further research agenda and developments.  The selected chapters cover a broad range of research issues, from theoretical approaches to case studies and best practices in the field.

 

Researcher, software developers, practitioners and students interested in the field of learning structure and schemas from documents will find the comprehensive coverage of this book useful for their research, academic, development and practice activity.

Content Level » Research

Keywords » Computational Intelligence - Document Analysis and Recognition - Schema Inference - Schema Integration - Structure Learning

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity

Table of contents 

From the content: Learning Structure and Schemas from Heterogeneous Domains in Networked Systems Surveyed.- Handling Hierarchically Structured Resources Addressing Interoperability Issues in Digital Libraries.- Administrative Document Analysis and Structure.- Automatic Document Layout Analysis through Relational Machine Learning.- Dataspaces: where structure and schema meet.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Computational Intelligence.