Inductive Logic Programming

26th International Conference, ILP 2016, London, UK, September 4-6, 2016, Revised Selected Papers

  • James Cussens
  • Alessandra Russo
Conference proceedings ILP 2016
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10326)

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Joana Côrte-Real, Inês Dutra, Ricardo Rocha
    Pages 1-13
  3. Marcin Malec, Tushar Khot, James Nagy, Erik Blask, Sriraam Natarajan
    Pages 14-26
  4. Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras
    Pages 27-39
  5. Phillip Odom, Raksha Kumaraswamy, Kristian Kersting, Sriraam Natarajan
    Pages 40-51
  6. Ute Schmid, Christina Zeller, Tarek Besold, Alireza Tamaddoni-Nezhad, Stephen Muggleton
    Pages 52-67
  7. Takayoshi Shoudai, Satoshi Matsumoto, Yusuke Suzuki
    Pages 68-80
  8. Ameet Soni, Dileep Viswanathan, Jude Shavlik, Sriraam Natarajan
    Pages 81-93
  9. Hai Dang Tran, Daria Stepanova, Mohamed H. Gad-Elrab, Francesca A. Lisi, Gerhard Weikum
    Pages 94-107
  10. Gustav Šourek, Suresh Manandhar, Filip Železný, Steven Schockaert, Ondřej Kuželka
    Pages 108-119
  11. Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmimala Saikia
    Pages 120-131
  12. Back Matter
    Pages 133-133

About these proceedings

Introduction

This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Conference on Inductive Logic Programming, ILP 2016, held in London, UK, in September 2016.

The 10 full papers presented were carefully reviewed and selected from 29 submissions. The papers represent well the current breath of ILP research topics such as predicate invention; graph-based learning; spatial learning; logical foundations; statistical relational learning; probabilistic ILP; implementation and scalability; applications in robotics, cyber security and games.

Keywords

Inductive logic programming Machine learning Relational learning Statistical relational learning Graph and tree learning Ontology learning Description logics Meta-learning Probabilistic logic learning

Editors and affiliations

  • James Cussens
    • 1
  • Alessandra Russo
    • 2
  1. 1.Department of Computer ScienceUniversity of YorkYorkUnited Kingdom
  2. 2.Imperial College LondonLondonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-63342-8
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-63341-1
  • Online ISBN 978-3-319-63342-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349