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International Conference on Algorithmic Learning Theory

ALT 2001: Algorithmic Learning Theory pp 315–331Cite as

Efficient Learning of Semi-structured Data from Queries

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2225))

Abstract

This paper studies the polynomial-time learnability of the classes of ordered gapped tree patterns (OGT) and ordered gapped forests (OGF) under the into-matching semantics in the query learning model of Angluin. The class OGT is a model of semi-structured database query languages, and a generalization of both the class of ordered/unordered tree pattern languages and the class of non-erasing regular pattern languages. First, we present a polynomial time learning algorithm for μ- OGT, the subclass of OGT without repeated tree variables, using equivalence queries and membership queries. By extending this algorithm, we present polynomial time learning algorithms for the classes μ-OGF of forests without repeated variables and OGT of trees with repeated variables using equivalence queries and subset queries. We also give representation-independent hardness results which indicate that both of equivalence and membership queries are necessary to learn μ-OGT.

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Author information

Authors and Affiliations

  1. Dept. of Informatics, Kyushu University, 812-8581, Fukuoka, Japan

    Hiroki Arimura, Hiroshi Sakamoto & Setsuo Arikawa

  2. PRESTO, Japan Science and Technology Co., Japan

    Hiroki Arimura

Authors
  1. Hiroki Arimura
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  2. Hiroshi Sakamoto
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  3. Setsuo Arikawa
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Editor information

Editors and Affiliations

  1. IBM, Thomas J.Watson Research Center, Room 33-237, P.O.Box 218, 10598, Yorktown, NY, USA

    Naoki Abe

  2. Dept.of Electrical Engineering and Computer Science, Tufts University, 161 College Ave., 02155, Medford, MA, USA

    Roni Khardon

  3. Medizinische Universität zu Lübeck,Inst.für Theoretische Informatik, Wallstr.40, 23560, Lübeck, Germany

    Thomas Zeugmann

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© 2001 Springer-Verlag Berlin Heidelberg

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Arimura, H., Sakamoto, H., Arikawa, S. (2001). Efficient Learning of Semi-structured Data from Queries. In: Abe, N., Khardon, R., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2001. Lecture Notes in Computer Science(), vol 2225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45583-3_24

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  • DOI: https://doi.org/10.1007/3-540-45583-3_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42875-6

  • Online ISBN: 978-3-540-45583-7

  • eBook Packages: Springer Book Archive

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