Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
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
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
A subterm-based global trie for tabled evaluation of logic programs
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
April: an inductive logic programming system
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Handling incomplete and complete tables in tabled logic programs
ICLP'06 Proceedings of the 22nd international conference on 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 an established sub-field of Machine Learning. Nevertheless, it is recognized that efficiency and scalability is a major obstacle to an increased usage of ILP systems in complex applications with large hypotheses spaces. In this work, we focus on improving the efficiency and scalability of ILP systems by exploring tabling mechanisms available in the underlying Logic Programming systems. Tabling is an implementation technique that improves the declarativeness and performance of Prolog systems by reusing answers to subgoals. To validate our approach, we ran the April ILP system in the YapTab Prolog tabling system using two well-known datasets. The results obtained show quite impressive gains without changing the accuracy and quality of the theories generated.