New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Prolog: the standard: reference manual
Prolog: the standard: reference manual
New Generation Computing - Special issue on inductive logic programming 97
Communications of the ACM
Term Indexing
Foundations of Logic Programming
Foundations of Logic Programming
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Relational Data Mining
Discovery of relational association rules
Relational Data Mining
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Data Mining and Knowledge Discovery
Associative-Commutative Discrimination Nets
TAPSOFT '93 Proceedings of the International Joint Conference CAAP/FASE on Theory and Practice of Software Development
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
k-RNN: k-relational nearest neighbour algorithm
Proceedings of the 2008 ACM symposium on Applied computing
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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
Generalization of clauses under implication
Journal of Artificial Intelligence Research
Faster association rules for multiple relations
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
April: an inductive logic programming system
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
ECML'05 Proceedings of the 16th European conference on Machine Learning
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
Partitional Clustering of Protein Sequences --- An Inductive Logic Programming Approach
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Compact lists for tabled evaluation
PADL'10 Proceedings of the 12th international conference on Practical Aspects of Declarative Languages
Conceptual clustering of multi-relational data
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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Inductive Logic Programming (ILP) is a powerful and well-developed abstraction for multi-relational data mining techniques. Despite the considerable success of ILP, deployed ILP systems still have efficiency problems when applied to complex problems. In this paper we propose a novel technique that avoids the procedure of deducing each example to evaluate each constructed clause. The technique is based on the Mode Directed Inverse Entailment approach to ILP, where a bottom clause is generated for each example and the generated clauses are subsets of the literals of such bottom clause. We propose to store in a prefix-tree all clauses that can be generated from all bottom clauses together with some extra information. We show that this information is sufficient to estimate the number of examples that can be deduced froma clause and present an ILP algorithmthat exploits this representation. We also present an extension of the algorithm where each prefix-tree is computed only once (compiled) per example. The evaluation of hypotheses requires only basic and efficient operations on trees. This proposal avoids re-computation of hypothesis' value in theorylevel search, in cross-validation evaluation procedures and in parameter tuning. Both proposals are empirically evaluated on real applications and considerable speedups were observed.