Communications of the ACM
Information Processing Letters
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Introduction to algorithms
On the efficiency of subsumption algorithms
Journal of the ACM (JACM)
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Principles of knowledge representation
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
Degrees of acyclicity for hypergraphs and relational database schemes
Journal of the ACM (JACM)
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Conjunctive query containment revisited
Theoretical Computer Science - Special issue on the 6th International Conference on Database Theory—ICDT '97
Conjunctive-query containment and constraint satisfaction
Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Learning logic programs with structured background knowledge
Artificial Intelligence
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Phase Transitions in Relational Learning
Machine Learning
Some Lower Bounds for the Computational Complexity of Inductive Logic Programming
ECML '93 Proceedings of the European Conference on Machine Learning
Learning Acyclic First-Order Horn Sentences from Entailment
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
On the Hardness of Learning Acyclic Conjunctive Queries
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Efficient Theta-Subsumption Based on Graph Algorithms
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
The Complexity of Acyclic Conjunctive Queries
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
Algorithms for acyclic database schemes
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
Effective rule induction from labeled graphs
Proceedings of the 2006 ACM symposium on Applied computing
A logic-based approach to relation extraction from texts
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
On finding acyclic subhypergraphs
FCT'05 Proceedings of the 15th international conference on Fundamentals of Computation Theory
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In recent years, it has been shown that methods from Inductive Logic Programming (ILP) are powerful enough to discover new first-order knowledge from data, while employing a clausal representation language that is relatively easy for humans to understand. Despite these successes, it is generally acknowledged that there are issues that present fundamental challenges for the current generation of systems. Among these, two problems are particularly prominent: learning deep clauses, i.e., clauses where a long chain of literals is needed to reach certain variables, and learning wide clauses, i.e., clauses with a large number of literals. In this paper we present a case study to show that by building on positive results on acyclic conjunctive query evaluation in relational database theory, it is possible to construct ILP learning algorithms that are capable of discovering clauses of significantly greater depth and width. We give a detailed description of the class of clauses we consider, describe a greedy algorithm to workwith these clauses, and show, on the popular ILP challenge problem of mutagenicity, how indeed our method can go beyond the depth and width barriers of current ILP systems.