Using regular tree automata as XML schemas

Chidlovskii, Boris

doi:10.1109/adl.2000.848373

Cited by 30 publications

(32 citation statements)

References 17 publications

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“…The proposed abstraction for the data definition section of archetypes is based on labeled trees which have been extensively used for modeling XML schemas and queries [20][28][37] [38]. Archetype specialization is modeled by a subsumption relation.…”

Section: Discussionmentioning

confidence: 99%

LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics

Maldonado

Moner

Boscá

et al. 2009

International Journal of Medical Informatics

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Purpose. To develop a powerful archetype editing framework capable of handling multiple reference models and oriented towards the semantic description and standardization of legacy data.Methods. The main prerequisite for implementing tools providing enhanced support for archetypes is the clear specification of archetype semantics. We propose a formalization of the definition section of archetypes based on types over tree-structured data. It covers the specialization of archetypes, the relationship between reference models and archetypes and conformance of data instances to archetypes.Results. LinkEHR-Ed a visual archetype editor based on the former formalization with advanced processing capabilities that supports multiple reference models, the editing and semantic validation of archetypes, the specification of mappings to data sources, and the automatic generation of data transformation scripts.Conclusions. LinkEHR-Ed is a useful tool for building, processing and validating archetypes based on any reference model.

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Section: Discussionmentioning

confidence: 99%

LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics

Maldonado

Moner

Boscá

et al. 2009

International Journal of Medical Informatics

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“…For instance, probabilistic tree grammars have been widely used to tackle ambiguity in natural language parsing (Charniak, 1993;Sima'an et al, 1996;Stolcke, 1995;Thorup, 1996) or to process structured text (Prescod, 2000;Murata, 1997;Chidlovskii, 2000). Context-free grammars (Hopcroft and Ullman, 1979) provide a traditional formalism that handles structural information.…”

Section: Introductionmentioning

confidence: 99%

Smoothing and compression with stochastic -testable tree languages

Rico-Juan¹,

Calera-Rubio²,

Carrasco³

2005

Pattern Recognition

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In this paper, we describe some techniques to learn probabilistic k-testable tree models, a generalization of the well known k-gram models, that can be used to compress or classify structured data. These models are easy to infer from samples and allow for incremental updates. Moreover, as shown here, backing-off schemes can be defined to solve data sparseness, a problem that often arises when using trees to represent the data. These features make them suitable to compress structured data files at a better rate than string-based methods.

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“…Now R is correct for L iff R * (L) ∩ Err = ∅, and R is normalizing for L iff L ⊆ R − * (Final), with Err and Final denoting the set of erroneous and final configurations, respectively. Starting from classical program analysis, recent applications include verification of XML transformations [3] and cryptographic protocols [10].…”

Section: Introductionmentioning

confidence: 99%