Information Processing Letters
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Equality and Domain Closure in First-Order Databases
Journal of the ACM (JACM)
Multistrategy Theory Revision: Induction and Abductionin INTHELEX
Machine Learning - Special issue on multistrategy learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Learning Logical Definitions from Relations
Machine Learning
Minimal Generalizations under OI-Implication
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
A Generalization Model Based on OI-implication for Ideal Theory Refinement
Fundamenta Informaticae - Intelligent Systems
OI-implication: soundness and refutation completeness
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Foundations of refinement operators for description logics
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Ideal downward refinement in the EL description logic
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
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Refinement operators for theories avoid the problems related to the myopia of many relational learning algorithms based on the operators that refine single clauses. However, the non-existence of ideal refinement operators has been proven for the standard clausal search spaces based on 0-subsumption or logical implication, which scales up to the spaces of theories. By adopting different generalization models constrained by the assumption of object identity, we extend the theoretical results on the existence of ideal refinement operators for spaces of clauses to the case of spaces of theories.