Knowledge Discovery Enhanced with Semantic and Social Information

  • Bettina Berendt
  • Dunja Mladenič
  • Marco de Gemmis
  • Giovanni Semeraro
  • Myra Spiliopoulou
  • Gerd Stumme
  • Vojtěch Svátek
  • Filip Železný

Part of the Studies in Computational Intelligence book series (SCI, volume 220)

Table of contents

  1. Front Matter
  2. Prior Conceptual Knowledge in Machine Learning and Knowledge Discovery

    1. Front Matter
      Pages 1-1
    2. Francesca A. Lisi, Floriana Esposito
      Pages 3-17
    3. Joanna Józefowska, Agnieszka Ławrynowicz, Tomasz Łukaszewski
      Pages 37-51
    4. Floriana Esposito, Nicola Fanizzi, Claudia d’Amato
      Pages 53-70
    5. Martin Labský, Vojtěch Svátek, Marek Nekvasil, Dušan Rak
      Pages 71-88
    6. Jan Rauch, Milan Šimůnek
      Pages 89-106
  3. Web Mining 2.0

    1. Front Matter
      Pages 107-107
    2. Linas Baltrunas, Francesco Ricci
      Pages 109-126
    3. Miha Grčar, Eva Klien, Blaž Novak
      Pages 127-143
  4. Back Matter

About this book


This book is a showcase of recent advances in knowledge discovery enhanced with semantic and social information. It includes eight contributed chapters that grew out of two joint workshops at ECML/PKDD 2007.
There is general agreement that the effectiveness of Machine Learning and Knowledge Discovery output strongly depends not only on the quality of source data and the sophistication of learning algorithms, but also on additional input provided by domain experts. There is less agreement on whether, when and how such input can and should be formalized as explicit prior knowledge.
The six chapters in the first part of the book aim to investigate this aspect by addressing four different topics: inductive logic programming; the role of human users; investigations of fully automated methods for integrating background knowledge; the use of background knowledge for Web mining. The two chapters in the second part are motivated by the Web 2.0 (r)evolution and the increasingly strong role of user-generated content. The contributions emphasize the vision of the Web as a social medium for content and knowledge sharing.


Web 2.0 algorithm algorithms clustering knowledge discovery learning logic logic programming machine machine learning ontology pattern mining programming semantic web mining

Editors and affiliations

  • Bettina Berendt
    • 1
  • Dunja Mladenič
    • 2
  • Marco de Gemmis
    • 3
  • Giovanni Semeraro
    • 3
  • Myra Spiliopoulou
    • 4
  • Gerd Stumme
    • 5
  • Vojtěch Svátek
    • 6
  • Filip Železný
    • 7
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium
  2. 2.J. Stefan InstituteLjubljanaSlovenia
  3. 3.Dipartimento di InformaticaUniversità degli Studi “Aldo Moro”BariItaly
  4. 4.ITI/FIN, Faculty of Computer ScienceOtto-von-Guericke-Universitaet MagdeburgMagdeburgGermany
  5. 5.Fachgebiet WissensverarbeitungUniversität KasselKasselGermany
  6. 6.University of Economics, PraguePraha 3Czech Republic
  7. 7.Faculty of Electrical Engineering, Department of CyberneticsCzech Technical UniversityPrague 6Czech Republic

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-01890-9
  • Online ISBN 978-3-642-01891-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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