Communications of the ACM
Information Processing Letters
Pattern languages are not learnable
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learning string patterns and tree patterns from examples
Proceedings of the seventh international conference (1990) on Machine learning
Polynomial-time inference of arbitrary pattern languages
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Anti-unification in constraint logics: foundations and applications to learnability in first-order logic, to speed-up learning, and to deduction
How many queries are needed to learn?
Journal of the ACM (JACM)
Logical settings for concept-learning
Artificial Intelligence
Exact learning of tree patterns from queries and counterexamples
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Exact learning of unordered tree patterns from queries
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Learning horn definitions: theory and an application to planning
New Generation Computing - Special issue on inductive logic programming 97
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
Machine Learning
Learning Unions of Tree Patterns Using Queries
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Learning a subclass of regular patterns in polynomial time
Theoretical Computer Science - Algorithmic learning theory
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Exact Learning of Finite Unions of Graph Patterns from Queries
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Learning of Finite Unions of Tree Patterns with Internal Structured Variables from Queries
IEICE - Transactions on Information and Systems
A polynomial time matching algorithm of ordered tree patterns having height-constrained variables
CPM'05 Proceedings of the 16th annual conference on Combinatorial Pattern Matching
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Certain and possible XPath answers
Proceedings of the 16th International Conference on Database Theory
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Tree patterns are natural candidates for representing rules and hypotheses in many tasks such as information extraction and symbolic mathematics. A tree pattern is a tree with labeled nodes where some of the leaves may be labeled with variables, whereas a tree instance has no variables. A tree pattern matches an instance if there is a consistent substitution for the variables that allows a mapping of subtrees to matching subtrees of the instance. A finite union of tree patterns is called a forest. In this paper, we study the learnability of tree patterns from queries when the subtrees are unordered. The learnability is determined by the semantics of matching as defined by the types of mappings from the pattern subtrees to the instance subtrees. We first show that unordered tree patterns and forests are not exactly learnable from equivalence and subset queries when the mapping between subtrees is one-to-one onto, regardless of the computational power of the learner. Tree and forest patterns are learnable from equivalence and membership queries for the one-to-one into mapping. Finally, we connect the problem of learning tree patterns to inductive logic programming by describing a class of tree patterns called Clausal trees that includes non-recursive single-predicate Horn clauses and show that this class is learnable from equivalence and membership queries.