COLT '94 Proceedings of the seventh annual conference on Computational learning theory
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Learning logic programs by using the product homomorphism method
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Learning atomic formulas with prescribed properties
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Hardness Results for Learning First-Order Representations and Programming by Demonstration
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A least common subsumer operation for an expressive description logic
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Computing Least Common Subsumers in Expressive Description Logics
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Conceptual Classifications Guided by a Concept Hierarchy
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Discovery of Maximal Analogies between Stories
DS '02 Proceedings of the 5th International Conference on Discovery Science
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
The description logic handbook
An algorithm based on counterfactuals for concept learning in the Semantic Web
Applied Intelligence
Approaches for semantic interoperability between domain ontologies
AOW '06 Proceedings of the second Australasian workshop on Advances in ontologies - Volume 72
Partial and Informative Common Subsumers in Description Logics
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
What's in an attribute? consequences for the least common subsumer
Journal of Artificial Intelligence Research
Pac-learning recursive logic programs: negative results
Journal of Artificial Intelligence Research
Learning with feature description logics
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Learnability of description logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Part-whole reasoning in an object-centered framework
Part-whole reasoning in an object-centered framework
Matching in hybrid terminologies
LPAR'07 Proceedings of the 14th international conference on Logic for programming, artificial intelligence and reasoning
Non-standard inferences in description logics
Non-standard inferences in description logics
A counterfactual-based learning algorithm for ALC description logic
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Building conceptual knowledge for managing learning paths in e-learning
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Towards constructing story databases using maximal analogies between stories
IHI'04 Proceedings of the 2004 international conference on Intuitive Human Interfaces for Organizing and Accessing Intellectual Assets
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Although there is an increasing amount of experimental research on learning concepts expressed in first-order logic, there are still relatively few formal results on the polynomial learnability of first-order representations from examples. Most previous analyses in the pac-model have focused on subsets of Prolog, and only a few highly restricted subsets have been shown to be learnable. In this paper, we will study instead the learnability of the restricted first-order logics known as “description logics”, also sometimes called “terminological logics” or “KL-ONE-type languages”. Description logics are also subsets of predicate calculus, but are expressed using a different syntax, allowing a different set of syntactic restrictions to be explored. We first define a simple description logic, summarize some results on its expressive power, and then analyze its learnability. It is shown that the full logic cannot be tractably learned. However, syntactic restrictions exist that enable tractable learning from positive examples alone, independent of the size of the vocabulary used to describe examples. The learnable sublanguage appears to be incomparable in expressive power to any subset of first-order logic previously known to be learnable.