Titlebook: Inductive Logic Programming; 14th International C Rui Camacho,Ross King,Ashwin Srinivasan Conference proceedings 2004 Springer-Verlag Berli

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Bottom-Up ILP Using Large Refinement Stepsinterpretations. The paper introduces a new implementation of the same base algorithm which gives several orders of magnitude speedup as well as extending the capabilities of the system. New tools include several fast engines for subsumption tests, handling real valued features, and pruning. We also

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On the Effect of Caching in Recursive Theory Learningate definition should be interleaved with the learning of the other ones in order to discover predicate dependencies. To overcome this problem we propose a variant of the separate-and-conquer strategy based on parallel learning of different predicate definitions. In order to improve its efficiency,

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FOIL-D: Efficiently Scaling FOIL for Multi-relational Data Mining of Large Datasetslational tables. Inductive logic programming (ILP) techniques have had considerable success on a variety of multi-relational rule mining tasks, however, most ILP systems do not scale to very large datasets. In this paper we present two extensions to a popular ILP system, FOIL, that improve its scala

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Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical In, a task that typically involves many more negative examples than positive examples. IE is the process of finding facts in unstructured text, such as biomedical journals, and putting those facts in an organized system. In particular, we have focused on learning to recognize instances of the protein-