Inductive Logic Programming

18th International Conference, ILP 2008 Prague, Czech Republic, September 10-12, 2008, Proceedings
Herausgegeben:Zelezný, Filip; Lavrac, Nada

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 5194
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 12512553, 978-3-540-85927-7
• 2008
• Seitenzahl: 372
• Erscheinungstermin: 5. September 2008
• Englisch
• Abmessung: 235mm x 155mm x 21mm
• Gewicht: 564g
• ISBN-13: 9783540859277
• ISBN-10: 3540859276
• Artikelnr.: 25047544

Autorenporträt

Prof. Dr. Nada Lavrac heads the Department of Knowledge Technologies at the Jo ef Stefan Institute in Ljubljana. She is the author and editor of several books and proceedings in the field of data mining and machine learning, and she has chaired or served on the boards of the main related journals and conferences. Her research interests include machine learning, data mining, and inductive logic programming, and related applications in medicine, public health, bioinformatics, and the management of virtual enterprises. In 1997 she was awarded the Ambassador of Science of Slovenia prize, and in 2007 she was elected as an ECCAI Fellow.

Inhaltsangabe

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 Applications.- Discriminative Structure Learning of Markov Logic Networks.- An Experiment in Robot Discovery with ILP.- Using the Bottom Clause and Mode Declarations on FOL Theory Revision from Examples.- DL-FOIL Concept Learning in Description Logics.- Feature Discovery with Type Extension Trees.- Feature Construction Using Theory-Guided Sampling and Randomised Search.- Foundations of Onto-Relational Learning.- L-Modified ILP Evaluation Functions for Positive-Only Biological Grammar Learning.- Logical Hierarchical Hidden Markov Models for Modeling User Activities.- Learning with Kernels in Description Logics.- Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms.- A Comparison between Two Statistical Relational Models.- Brave Induction.- A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases.- A Note on Refinement Operators for IE-Based ILP Systems.- Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach.- Learning Block-Preserving Outerplanar Graph Patterns and Its Application to Data Mining.