Inductive Logic Programming

27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers

  • Nicolas Lachiche
  • Christel Vrain
Conference proceedings ILP 2017

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10759)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10759)

Table of contents

  1. Front Matter
    Pages I-X
  2. Laura Antanas, Anton Dries, Plinio Moreno, Luc De Raedt
    Pages 1-15
  3. Joana Côrte-Real, Inês Dutra, Ricardo Rocha
    Pages 31-45
  4. Wang-Zhou Dai, Stephen Muggleton, Jing Wen, Alireza Tamaddoni-Nezhad, Zhi-Hua Zhou
    Pages 46-62
  5. Sebastijan Dumančić, Hendrik Blockeel
    Pages 63-77
  6. Nikos Katzouris, Alexander Artikis, Georgios Paliouras
    Pages 78-93
  7. Navdeep Kaur, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, Sriraam Natarajan
    Pages 94-111
  8. Hiroyuki Nishiyama, Hayato Ohwada
    Pages 112-123
  9. Tony Ribeiro, Sophie Tourret, Maxime Folschette, Morgan Magnin, Domenico Borzacchiello, Francisco Chinesta et al.
    Pages 124-139
  10. Gustav Šourek, Martin Svatoš, Filip Železný, Steven Schockaert, Ondřej Kuželka
    Pages 140-151
  11. Martin Svatoš, Gustav Šourek, Filip Železný, Steven Schockaert, Ondřej Kuželka
    Pages 152-168
  12. Lovekesh Vig, Ashwin Srinivasan, Michael Bain, Ankit Verma
    Pages 169-183
  13. Back Matter
    Pages 185-185

About these proceedings


This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017.
The 12 full papers presented were carefully reviewed and selected from numerous submissions.
Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.


artificial intelligence computer programming data mining image processing inductive logic programming learning algorithms machine learning probabilistic graphical models programming languages relaltional learning relational data mining rule learning semantics statistical relational learning

Editors and affiliations

  • Nicolas Lachiche
    • 1
  • Christel Vrain
    • 2
  1. 1.University of StrasbourgStrasbourgFrance
  2. 2.University of OrléansOrléansFrance

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-78089-4
  • Online ISBN 978-3-319-78090-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site