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Approximate Structural Consistency

  • Michel de Rougemont
  • Adrien Vieilleribière
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5901)

Abstract

We consider documents as words and trees on some alphabet Σ and study how to compare them with some regular schemas on an alphabet Σ′. Given an input document I, we decide if it may be transformed into a document J which is ε-close to some target schema T: we show that this approximate decision problem can be efficiently solved. In the simple case where the transformation is the identity, we describe an approximate algorithm which decides if I is close to a target regular schema (DTD). This property is testable, i.e. can be solved in time independent of the size of the input document, by just sampling I. In the general case, the Structural Consistency decides if there is a transducer \(\mathcal {T}\) with at most m states such that I is ε-close to I′ and his image \(\mathcal {T}(I')\) is both close to T and of size comparable to the size of I. We show that Structural Consistency is also testable, i.e. can be solved by sampling I.

Keywords

Regular Expression Regular Language Target Schema Simple Loop Structural Consistency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Michel de Rougemont
    • 1
  • Adrien Vieilleribière
    • 1
  1. 1.Université Paris II, & LRI CNRS LRIOrsayFrance

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