Inductive Logic Programming

18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008 Proceedings

  • Editors
  • Filip Železný
  • Nada Lavrač
Conference proceedings ILP 2008
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5194)

Table of contents

  1. Front Matter
  2. Invited Talks

  3. Research Papers

    1. Annalisa Appice, Michelangelo Ceci, Donato Malerba
      Pages 24-41
    2. Mark Bartlett, Iain Bate, Dimitar Kazakov
      Pages 42-58
    3. Marenglen Biba, Stefano Ferilli, Floriana Esposito
      Pages 59-76
    4. Gregor Leban, Jure Žabkar, Ivan Bratko
      Pages 77-90
    5. Ana Luísa Duboc, Aline Paes, Gerson Zaverucha
      Pages 91-106
    6. Nicola Fanizzi, Claudia d’Amato, Floriana Esposito
      Pages 107-121
    7. Paolo Frasconi, Manfred Jaeger, Andrea Passerini
      Pages 122-139
    8. Sachindra Joshi, Ganesh Ramakrishnan, Ashwin Srinivasan
      Pages 140-157
    9. Francesca A. Lisi, Floriana Esposito
      Pages 158-175
    10. Thierry Mamer, Christopher H. Bryant, John McCall
      Pages 176-191
    11. Sriraam Natarajan, Hung H. Bui, Prasad Tadepalli, Kristian Kersting, Weng-Keen Wong
      Pages 192-209
    12. Nicola Fanizzi, Claudia d’Amato, Floriana Esposito
      Pages 210-225

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, held in Prague, Czech Republic, in September 2008.

The 20 revised full papers presented together with the abstracts of 5 invited lectures were carefully reviewed and selected during two rounds of reviewing and improvement from 46 initial submissions. All current topics in inductive logic programming are covered, ranging from theoretical and methodological issues to advanced applications. The papers present original results in the first-order logic representation framework, explore novel logic induction frameworks, and address also new areas such as statistical relational learning, graph mining, or the semantic Web.

Keywords

Bayesian networks algorithmic learning biological grammar induction classifier systems clustering data mining decision-tree learning efficiency generalisation operators graph mining graph structured pattern higher-order logic i implementation

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-85928-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-85927-7
  • Online ISBN 978-3-540-85928-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book