Learning regular sets from queries and counterexamples
Information and Computation
Macro tree transducers, attribute grammars, and MSO definable tree translations
Information and Computation
Minimization algorithms for sequential transducers
Theoretical Computer Science
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A formal model for an expressive fragment of XSLT
Information Systems - Databases: Creation, management and utilization
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimizing subsequential transducers: a survey
Theoretical Computer Science
Information Processing Letters
A Machine Learning Approach to Rapid Development of XML Mapping Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
XML type checking with macro tree transducers
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the tree-transformation power of XSLT
Acta Informatica
Interactive learning of node selecting tree transducer
Machine Learning
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Learning deterministic regular expressions for the inference of schemas from XML data
Proceedings of the 17th international conference on World Wide Web
Computational Linguistics
Deciding equivalence of top--down XML transformations in polynomial time
Journal of Computer and System Sciences
XSLT version 2.0 is turing-complete: a purely transformation based proof
CIAA'06 Proceedings of the 11th international conference on Implementation and Application of Automata
Minimization of deterministic bottom-up tree transducers
DLT'10 Proceedings of the 14th international conference on Developments in language theory
Normalization of sequential top-down tree-to-word transducers
LATA'11 Proceedings of the 5th international conference on Language and automata theory and applications
Learning twig and path queries
Proceedings of the 15th International Conference on Database Theory
DLT'12 Proceedings of the 16th international conference on Developments in Language Theory
Learning queries for relational, semi-structured, and graph databases
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
Proceedings of the 22nd international conference on World Wide Web companion
Query induction with schema-guided pruning strategies
The Journal of Machine Learning Research
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A generalization from string to trees and from languages to translations is given of the classical result that any regular language can be learned from examples: it is shown that for any deterministic top-down tree transformation there exists a sample set of polynomial size (with respect to the minimal transducer) which allows to infer the translation. Until now, only for string transducers and for simple relabeling tree transducers, similar results had been known. Learning of deterministic top-down tree transducers (dtops) is far more involved because a dtop can copy, delete, and permute its input subtrees. Thus, complex dependencies of labeled input to output paths need to be maintained by the algorithm. First, a Myhill-Nerode theorem is presented for dtops, which is interesting on its own. This theorem is then used to construct a learning algorithm for dtops. Finally, it is shown how our result can be applied to xml transformations (e.g. xslt programs). For this, a new dtd-based encoding of unranked trees by ranked ones is presented. Over such encodings, dtops can realize many practically interesting xml transformations which cannot be realized on firstchild/next-sibling encodings.