Learning Sequential Tree-to-Word Transducers

Laurence, G; Lemay, Aurélien; Niehren, Joachim; Staworko, Sławek; Tommasi, Marc

doi:10.1007/978-3-319-04921-2_40

Cited by 18 publications

(14 citation statements)

References 17 publications

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“…Furthermore, using a Myhill-Nerode characterization, a normal form computable in Exptime is shown to exist. This normal form was later proven to be learnable in Ptime [8].…”

Section: Introductionmentioning

confidence: 78%

Normal Form on Linear Tree-to-Word Transducers

Boiret

2016

Language and Automata Theory and Applications

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We study a subclass of tree-to-word transducers: linear treeto-word transducers, that cannot use several copies of the input. We aim to study the equivalence problem on this class, by using minimization and normalization techniques. We identify a Myhill-Nerode characterization. It provides a minimal normal form on our class, computable in Exptime. This paper extends an already existing result on tree-to-word transducers without copy or reordering (sequential tree-to-word transducers), by accounting for all the possible reorderings in the output.

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“…Furthermore, using a Myhill-Nerode characterization, a normal form computable in Exptime is shown to exist. This normal form was later proven to be learnable in Ptime [8].…”

Section: Introductionmentioning

confidence: 78%

Normal Form on Linear Tree-to-Word Transducers

Boiret

2016

Language and Automata Theory and Applications

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“…Second, we showed that if building a fully earliest transducer is potentially exponential, our reduction only requires quasi-periodic states to be earliest, which can be done in polynomial time. The use of the equivalence problem for morphisms on a CFG [13] and of properties on straight-line programs [10] is essential here as it was in [7,8]. This leads to further research questions, starting with generalization of this result to all tree-to-words transducers.…”

Section: Resultsmentioning

confidence: 99%

Deciding Equivalence of Linear Tree-to-Word Transducers in Polynomial Time

Boiret¹,

Palenta²

2016

Developments in Language Theory

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We show that the equivalence of deterministic linear top-down treeto-word transducers is decidable in polynomial time. Linear tree-to-word transducers are non-copying but not necessarily order-preserving and can be used to express XML and other document transformations. The result is based on a partial normal form that provides a basic characterization of the languages produced by linear tree-to-word transducers.

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“…The learnability definition that we have used in [10] is inspired by grammatical inference [19] i.e., the branch of machine learning that aims at constructing a formal grammar by generalizing a set of examples. Grammatical inference techniques have been recently employed in the context of XML for learning queries [13,25,31], schemas [8,9,16], and transformations [23,24]. More precisely, in [10] we have used a variant of the classical grammatical inference framework (i.e., language identification in the limit with polynomial time and data [21]) that takes into account the intractability of deciding the consistency of a set of examples (in the spirit of [23]).…”

Section: Related Workmentioning

confidence: 99%

A Paradigm for Learning Queries on Big Data

Bonifati

Ciucanu

Lemay

et al. 2014

Proceedings of the First International Workshop on Bringing the Value of "Big Data" to Users (Data4U 2014)

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International audienceSpecifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder as it requires the users to deal with database instances of big sizes and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this paper, we propose a novel paradigm for interactive learning of queries on big data, without assuming any knowledge of the database schema. The paradigm can be applied to different database models and a class of queries adequate to the database model. In particular, in this paper we present two instantiations that validated the proposed paradigm for learning relational join queries and for learning path queries on graph databases. Finally, we discuss the challenges of employing the paradigm for further data models and for learning cross-model schema mappings

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Learning Sequential Tree-to-Word Transducers

Cited by 18 publications

References 17 publications

Normal Form on Linear Tree-to-Word Transducers

Normal Form on Linear Tree-to-Word Transducers

Deciding Equivalence of Linear Tree-to-Word Transducers in Polynomial Time

A Paradigm for Learning Queries on Big Data

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