Wrapper Maintenance: A Machine Learning Approach

TL;DR

This paper introduces a machine learning-based approach for maintaining web data extraction wrappers, focusing on verification and reinduction to adapt to web source changes, validated through extensive real-world testing.

Contribution

It presents a novel algorithm that learns from positive examples to support wrapper verification and automatic reinduction, addressing a key challenge in web data extraction maintenance.

Findings

High accuracy in detecting wrapper changes (recall 0.95)

Effective reinduction with precision 0.90 and recall 0.80

Validated over a year with 27 wrappers and 10 web sources.

Abstract

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task.