Structural inference for semistructured data

Sankey, Jason; Wong, Raymond K.

doi:10.1145/502611.502613

Cited by 2 publications

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“…Other relevant DTD inference research is Sankey and Wong [2001] and Chidlovskii [2001], which learn finite automata but do not consider the translation to deterministic regular expressions. Also, in Young-Lai and Tompa [2000] a method is proposed to infer DTDs through stochastic grammars where righthand sides of rules are represented by probabilistic automata.…”

Section: Related Workmentioning

confidence: 99%

Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data

et al. 2010

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Inferring an appropriate DTD or XML Schema Definition (XSD) for a given collection of XML documents essentially reduces to learning deterministic regular expressions from sets of positive example words. Unfortunately, there is no algorithm capable of learning the complete class of deterministic regular expressions from positive examples only, as we will show. The regular expressions occurring in practical DTDs and XSDs, however, are such that every alphabet symbol occurs only a small number of times. As such, in practice it suffices to learn the subclass of deterministic regular expressions in which each alphabet symbol occurs at most k times, for some small k. We refer to such expressions as k-occurrence regular expressions (k-OREs for short). Motivated by this observation, we provide a probabilistic algorithm that learns k-OREs for increasing values of k, and selects the deterministic one that best describes the sample based on a Minimum Description Length argument. The effectiveness of the method is empirically validated both on real world and synthetic data. Furthermore, the method is shown to be conservative over the simpler classes of expressions considered in previous work.

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Section: Related Workmentioning

confidence: 99%

Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data

et al. 2010

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“…As described above, DataGuides use graphs as the general form to represent the common structures of XML collections. DataGuides, however, are not as expressive as Data Type Definitions (DTDs) (Bex et al, 2005;Bex et al, 2006;Chidlovskii, 2001;Garofalakis et al, 2003;Min, Ahn, & Chung, 2003;Sankey & Wong, 2001) or XML Schema (Bex et al, 2007;Bex et al, 2008;Clark;Hegewald et al, 2006), the two most widely used formats to represent the structure of XML documents.…”

Section: Dtd and Xml Schema Extraction And Inferencementioning

confidence: 99%

XML Data Integration

Cao

Yan

Candan

et al. 2010

Advanced Applications and Structures in XML Processing

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Many applications require exchange and integration of data from multiple, heterogeneous sources. eXtensible Markup Language (XML) is a standard developed to satisfy the convenient data exchange needs of these applications. However, XML by itself does not address the data integration requirements. This chapter discusses the challenges and techniques in XML Data Integration. It first presents a four step outline, illustrating the steps involved in the integration of XML data. This chapter, then, focuses on the first two of these steps: schema extraction and data/schema mapping. More specifically, schema extraction presents techniques to extract tree summaries, DTDs, or XML Schemas from XML documents. The discussion on data/schema mapping focuses on techniques for aligning XML data and schemas.

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Structural inference for semistructured data

Cited by 2 publications

References 0 publications

Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data

Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data

XML Data Integration

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