Inductive Logic Programming

1. Front Matter

   PDF

2. Invited Talks

   1. Building Theories of the World: Human and Machine Learning Perspectives

      • Joshua B. Tenenbaum

      Pages 1-1

   2. SRL without Tears: An ILP Perspective

      • Kristian Kersting

      Pages 2-2

   3. Semantic Web Meets ILP: Unconsumated Love, or No Love Lost?

      • Frank van Harmelen

      Pages 3-3

   4. Learning Expressive Models of Gene Regulation

      • Mark Craven

      Pages 4-4

   5. Information Overload and FP7 Funding Opportunities in 2009-10

      • Stefano Bertolo

      Pages 5-5

3. Research Papers

   1. A Model to Study Phase Transition and Plateaus in Relational Learning

      • Erick Alphonse, Aomar Osmani

      Pages 6-23

   2. Top-Down Induction of Relational Model Trees in Multi-instance Learning

      • Annalisa Appice, Michelangelo Ceci, Donato Malerba

      Pages 24-41

   3. Challenges in Relational Learning for Real-Time Systems Applications

      • Mark Bartlett, Iain Bate, Dimitar Kazakov

      Pages 42-58

   4. Discriminative Structure Learning of Markov Logic Networks

      • Marenglen Biba, Stefano Ferilli, Floriana Esposito

      Pages 59-76

   5. An Experiment in Robot Discovery with ILP

      • Gregor Leban, Jure Žabkar, Ivan Bratko

      Pages 77-90

   6. Using the Bottom Clause and Mode Declarations on FOL Theory Revision from Examples

      • Ana Luísa Duboc, Aline Paes, Gerson Zaverucha

      Pages 91-106

   7. DL-FOIL Concept Learning in Description Logics

      • Nicola Fanizzi, Claudia d’Amato, Floriana Esposito

      Pages 107-121

   8. Feature Discovery with Type Extension Trees

      • Paolo Frasconi, Manfred Jaeger, Andrea Passerini

      Pages 122-139

   9. Feature Construction Using Theory-Guided Sampling and Randomised Search

      • Sachindra Joshi, Ganesh Ramakrishnan, Ashwin Srinivasan

      Pages 140-157

   10. Foundations of Onto-Relational Learning

       • Francesca A. Lisi, Floriana Esposito

       Pages 158-175

   11. L-Modified ILP Evaluation Functions for Positive-Only Biological Grammar Learning

       • Thierry Mamer, Christopher H. Bryant, John McCall

       Pages 176-191

   12. Logical Hierarchical Hidden Markov Models for Modeling User Activities

       • Sriraam Natarajan, Hung H. Bui, Prasad Tadepalli, Kristian Kersting, Weng-Keen Wong

       Pages 192-209

   13. Learning with Kernels in Description Logics

       • Nicola Fanizzi, Claudia d’Amato, Floriana Esposito

       Pages 210-225

   14. Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms

       • Simon Price, Peter Flach

       Pages 226-243

The 18th International Conference on Inductive Logic Programming was held in Prague, September 10–12, 2008. ILP returned to Prague after 11 years, and it is tempting to look at how the topics of interest have evolved during that time. The ILP community clearly continues to cherish its beloved ?rst-order logic representation framework. This is legitimate, as the work presented at ILP 2008 demonstrated that there is still room for both extending established ILP approaches (such as inverse entailment) and exploring novel logic induction frameworks (such as brave induction). Besides the topics lending ILP research its unique focus, we were glad to see in this year’s proceedings a good n- ber of papers contributing to areas such as statistical relational learning, graph mining, or the semantic web. To help open ILP to more mainstream research areas, the conference featured three excellent invited talks from the domains of the semantic web (Frank van Harmelen), bioinformatics (Mark Craven) and cognitive sciences (Josh Tenenbaum). We deliberately looked for speakers who are not directly involved in ILP research. We further invited a tutorial on stat- tical relational learning (Kristian Kersting) to meet the strong demand to have the topic presented from the ILP perspective. Lastly, Stefano Bertolo from the European Commission was invited to give a talk on the ideal niches for ILP in the current EU-supported research on intelligent content and semantics.

• 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
• algorithm analysis and problem complexity

Policies and ethics