Learning k-Occurrence Regular Expressions from Positive and Negative Samples

Yeting Li,Haiming Chen,Xiaoying Mou

doi:10.1007/978-3-030-33223-5_22

Learning k-Occurrence Regular Expressions from Positive and Negative Samples

Yeting Li, Haiming Chen + Show 1 more

Open Access

https://doi.org/10.1007/978-3-030-33223-5_22

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Publication Date: Jan 1, 2019

Affiliation: Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences

#Deterministic Regular Expressions #Negative Samples + Show 8 more

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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.

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