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Learning k-Occurrence Regular Expressions from Positive and Negative Samples

  • Yeting Li
  • Xiaoying Mou
  • Haiming ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11788)

Abstract

Deterministic regular expressions (DREs) are a core part of XML schema languages such as DTD/XSD and are used in different kinds of applications. Presently the most powerful model to learn DREs is k-occurrence regular expressions (k-OREs for short). However, there has been no algorithms can learn k-OREs from positive and negative samples. In this paper, we propose an efficient and effective algorithm to learn k-OREs from positive and negative samples. Our algorithm proceeds as follows: (1) learning deterministic k-OA from positive and negative samples based on genetic algorithm; (2) converting the k-OA into optimum deterministic k-OREs.

Keywords

XML schema Deterministic regular expressions Language learning Positive and negative samples 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Computer Science, Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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