Communications of the ACM
Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
A four-valued semantics for terminological logics
Artificial Intelligence
CLASSIC: a structural data model for objects
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Learning Nested Differences of Intersection-Closed Concept Classes
Machine Learning
Learning boolean functions in an infinite attribute space
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
LaSSIE: a knowledge-based software information system
Communications of the ACM - Special issue on software engineering
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Machine Learning
Classification as a Query Processing Technique in the CANDIDE Semantic Data Model
Proceedings of the Fifth International Conference on Data Engineering
Variant Construction Using Constraint Propagation Techniques over Semantic Networks
5. Österreichische Artificial Intelligence-Tagung
Data Models in Knowledge Representation System: A Case Study
GWAI-86 und 2. Österreichische Artificial-Intelligence-Tagung
Entity-Situation: A Model for the Knowledge Representation Module of a KBMS
EDBT '88 Proceedings of the International Conference on Extending Database Technology: Advances in Database Technology
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
COLT '96 Proceedings of the ninth 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)
Corrigendum for "learnability of description logics"
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Polynomial-time learnability of logic programs with local variables from entailment
Theoretical Computer Science - Algorithmic learning theory
Learning from Entailment of Logic Programs with Local Variables
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
DL-FOIL Concept Learning in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Cryptographic limitations on learning one-clause logic programs
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Learnability in inductive logic programming: some basic results and techniques
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Learnability of simply-moded logic programs from entailment
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
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This paper considers the learnability of subsets of first-order logic. Piror work has established two boundaries of learnability: Haussler [1989] has shown that conjunctions in first-order logic cannot be learned in the Valiant model, even if the form of the conjunction is highly restricted; on the other hand, Valiant [1984] has shown that propositional conjunctions are learnable. In this paper, we study the learnability of the restricted first-order logics known as description logics. 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. In this paper, 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 that enable tractable learning exist. The learnability results hold even if the alphabets of primitive classes and roles (over which descriptions are constructed) are infinite; our positive result thus generalizes not only the result of Valiant [1984] on learning monomials to learning concepts in our (conjunctive) first order language, but also the result of Blum [1990] on learning monomials over infinite attribute spaces.