Learning Concise Relax NG Schemas Supporting Interleaving from XML Documents

Li, Yeting; Mou, Xiaoying; Chen, Haiming

doi:10.1007/978-3-030-05090-0_26

Cited by 7 publications

(12 citation statements)

References 23 publications

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“…Learning Relax NG schemas is an important research problem for schema inference, since it is more powerful than other XML schema languages, such as DTD or XSD [5] and has unrestricted supports for the interleaving operator. It is known that the essential task in Relax NG schema inference is learning RE(&) from a set of given sample [23,31].…”

Section: Introductionmentioning

confidence: 99%

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Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

Chen

Zhang

et al. 2020

Advances in Knowledge Discovery and Data Mining

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The presence of a schema for XML documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a schema or a valid schema. Therefore, it is essential to devise algorithms to infer schemas. The fundamental task in XML schema inference is to learn regular expressions. In this paper, we focus on learning the subclass of RE(&) called SIREs (the subclass of regular expressions with interleaving). Previous work in this direction lacks inference algorithms that support inference from positive and negative examples. We provide an algorithm to learn SIREs from positive and negative examples based on genetic algorithms and parallel techniques. Our algorithm also has better expansibility, which means that our algorithm not only supports learning with positive and negative examples, but also supports learning with positive or negative examples only. Experimental results demonstrate the effectiveness of our algorithm.

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Section: Introductionmentioning

confidence: 99%

“…Previously, RE(&) learning has been studied from positive examples only [23,29,31]. However, negative examples might be useful in some applications.…”

Section: Introductionmentioning

confidence: 99%

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

Chen

Zhang

et al. 2020

Advances in Knowledge Discovery and Data Mining

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“…The presence of a schema for XML documents has many advantages, such as for query processing and optimization, development of database applications, data integration and exchange [15,42,34,18]. However, many XML documents in practice are not accompanied by a (valid) schema [3,37,36,6,41,25], making schema inference an attractive research problem [2,5,7,17,22,43,13,30,32]. Studying schema inference also has several practical motivations.…”

Section: Introductionmentioning

confidence: 99%

“…In such cases the interleaving is necessary. Here we list the more recent efforts on RE(&) inference (see [13,40,43,30,32]). The aim of these approaches is to infer restricted subclasses of single occurrence RE(&), in which each symbol occurs at most once, starting from a positive set of words.…”

Section: Introductionmentioning

confidence: 99%

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An effective algorithm for learning single occurrence regular expressions with interleaving

Chen

Zhang

et al. 2019

Proceedings of the 23rd International Database Applications &Amp; Engineering Symposium on - IDEAS '19

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The advantages offered by the presence of a schema are numerous. However, many XML documents in practice are not accompanied by a (valid) schema, making schema inference an attractive research problem. The fundamental task in XML schema learning is inferring restricted subclasses of regular expressions. Most previous work either lacks support for interleaving or only has limited support for interleaving. In this paper, we first propose a new subclass Single Occurrence Regular Expressions with Interleaving (SOIRE), which has unrestricted support for interleaving. Then, based on single occurrence automaton and maximum independent set, we propose an algorithm iSOIRE to infer SOIREs. Finally, we further conduct a series of experiments on real datasets to evaluate the effectiveness of our work, comparing with both ongoing learning algorithms in academia and industrial tools in real-world. The results reveal the practicability of SOIRE and the effectiveness of iSOIRE, showing the high preciseness and conciseness of our work.

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Learning k-Occurrence Regular Expressions with Interleaving

Zhang

Cao

et al. 2019

Database Systems for Advanced Applications

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Learning Concise Relax NG Schemas Supporting Interleaving from XML Documents

Cited by 7 publications

References 23 publications

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

An effective algorithm for learning single occurrence regular expressions with interleaving

Learning k-Occurrence Regular Expressions with Interleaving

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