Inductive Logic Programming: 18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008, Proceedings [Paperback]

The 18th International Conference on Inductive Logic Programming was held in Prague, September 1012, 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 years 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.Invited Talks.- Building Theories of the World: Human and Machine Learning Perspectives.- SRL without Tears: An ILP Perspective.- Semantic Web Meets ILP: Unconsumated Love, or No Love Lost?.- Learning Expressive Models of Gene Regulation.- Information Overload and FP7 Funding Opportunities in 2009-10.- Research Papers.- A Model to Study Phase Transition and Plateaus in Relational Learning.- Top-Down Induction of Relational Model Trees in Multi-instance Learning.- Challenges in Relational Learning for Real-Time Systems AppliclC�