Artificial Intelligence
NP-completeness of the set unification and matching problems
Proc. of the 8th international conference on Automated deduction
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Relational learning as search in a critical region
The Journal of Machine Learning Research
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Proceedings of the 25th international conference on Machine learning
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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
Demand-driven indexing of prolog clauses
ICLP'07 Proceedings of the 23rd international conference on Logic programming
ProGolem: a system based on relative minimal generalisation
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
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Entailment is an important problem in computational logic particularly relevant to the Inductive Logic Programming (ILP) community as it is at the core of the hypothesis coverage test which is often the bottleneck of an ILP system. Despite developments in resolution heuristics and, more recently, in subsumption engines, most ILP systems simply use Prolog's left-to-right, depth-first search selection function for SLD-resolution to perform the hypothesis coverage test. We implemented two alternative selection functions for SLD-resolution: smallest predicate domain (SPD) and smallest variable domain (SVD); and developed a subsumption engine, Subsumer.These entailment engines were fully integrated into the ILP system ProGolem. The performance of these four entailment engines is compared on a representative set of ILP datasets. As expected, on determinate datasets Prolog's built-in resolution, is unrivalled. However, in the presence of even little non-determinism, its performance quickly degrades and a sophisticated entailment engine is required.