Learning Structure and Schemas from Documents

  • Marenglen Biba
  • Fatos Xhafa
Part of the Studies in Computational Intelligence book series (SCI, volume 375)

Table of contents

  1. Front Matter
  2. Abdel Belaïd, Vincent Poulain D’Andecy, Hatem Hamza, Yolande Belaïd
    Pages 51-71
  3. Stefano Ferilli, Teresa M. A. Basile, Nicola Di Mauro, Floriana Esposito
    Pages 73-96
  4. Maurizio Atzori, Nicoletta Dessì
    Pages 97-119
  5. Michelangelo Ceci, Corrado Loglisci, Donato Malerba
    Pages 121-142
  6. Andrea Addis, Giuliano Armano, Eloisa Vargiu
    Pages 143-163
  7. Vavilis Sokratis, Ergina Kavallieratou, Roberto Paredes, Kostas Sotiropoulos
    Pages 165-179
  8. Simone Marinai, Beatrice Miotti, Giovanni Soda
    Pages 181-204
  9. Hui Yang, Rajesh Swaminathan, Abhishek Sharma, Vilas Ketkar, Jason D‘Silva
    Pages 205-225
  10. Xin Zhang, Pamela Thompson, Zbigniew W. Raś, Pawel Jastreboff
    Pages 227-245
  11. Angela Locoro, Daniele Grignani, Viviana Mascardi
    Pages 315-341
  12. Emanuele Salerno, Pasquale Savino, Anna Tonazzini
    Pages 343-367
  13. Janusz Wojtusiak, Ancha Baranova
    Pages 369-384
  14. Gianni Costa, Alfredo Cuzzocrea, Giuseppe Manco, Riccardo Ortale
    Pages 385-412
  15. Andrea Manconi, Patricia Rodriguez-Tomé
    Pages 413-432

About this book

Introduction

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.

Keywords

Computational Intelligence Computational Intelligence Computational Intelligence Document Analysis and Recognition Document Analysis and Recognition Document Analysis and Recognition Schema Inference Schema Inference Schema Inference Schema Integration Schema Integration Schema Integration Structure Learning Structure Learning Structure Learning

Editors and affiliations

  • Marenglen Biba
    • 1
  • Fatos Xhafa
    • 2
  1. 1.University of New York TiranaTiranaAlbania
  2. 2.Technical University of CataloniaBarcelonaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-22913-8
  • Copyright Information Springer-Verlag GmbH Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-22912-1
  • Online ISBN 978-3-642-22913-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book