Discovery of relational association rules
Relational Data Mining
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
A logical approach to reasoning by analogy
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Lattice-search runtime distributions may be heavy-tailed
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
On applying tabling to inductive logic programming
ECML'05 Proceedings of the 16th European conference on Machine Learning
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Efficient and Scalable Induction of Logic Programs Using a Deductive Database System
Inductive Logic Programming
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
PADL '09 Proceedings of the 11th International Symposium on Practical Aspects of Declarative Languages
Parallel ILP for distributed-memory architectures
Machine Learning
A Term-Based Global Trie for Tabled Logic Programs
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Spatial-yap: a logic-based geographic information system
ICLP'07 Proceedings of the 23rd international conference on Logic programming
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Interactive discriminative mining of chemical fragments
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
On improving the efficiency and robustness of table storage mechanisms for tabled evaluation
PADL'07 Proceedings of the 9th international conference on Practical Aspects of Declarative Languages
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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Inductive Logic Programming (ILP) is a Machine Learning research field that has been quite successful in knowledge discovery in relational domains. ILP systems use a set of pre-classified examples (positive and negative) and prior knowledge to learn a theory in which positive examples succeed and the negative examples fail. In this paper we present a novel ILP system called April, capable of exploring several parallel strategies in distributed and shared memory machines.