An Effective Algorithm for Learning Single Occurrence Regular Expressions with Interleaving

TL;DR

This paper introduces iSOIRE, an algorithm for inferring Single Occurrence Regular Expressions with Interleaving (SOIRE), supporting unrestricted interleaving, and demonstrates its effectiveness through experiments on real datasets.

Contribution

The paper proposes a novel subclass SOIRE with full interleaving support and an algorithm iSOIRE for inferring these expressions from XML data.

Findings

iSOIRE achieves high precision and conciseness.

Experimental results outperform existing learning algorithms.

SOIRE is practical for real-world XML schema inference.

Abstract

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.