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Learning XML Grammars

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2123))

Abstract

We sketch possible applications of grammatical inference techniques to problems arising in the context of XML. The idea is to infer document type definitions (DTDs) of XML documents in situations either when the original DTD is missing or when a DTD should be (re) designed or when a DTD should be restricted to a more user-oriented view on a subset of the (given) DTD. The usefulness of such an approach is underlined by the importance of knowing appropriate DTDs; this knowledge can be exploited, e.g., for optimizing database queries based on XML.

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Author information

Authors and Affiliations

  1. Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Sand 13, D-72076, Tübingen, Germany

    Henning Fernau

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  1. Henning Fernau
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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© 2001 Springer-Verlag Berlin Heidelberg

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Fernau, H. (2001). Learning XML Grammars. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_7

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  • DOI: https://doi.org/10.1007/3-540-44596-X_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42359-1

  • Online ISBN: 978-3-540-44596-8

  • eBook Packages: Springer Book Archive

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