Reverse method for labeling the information from semi-structured web pages

TL;DR

This paper introduces a reverse labeling technique for extracting structured data from semi-structured web pages, leveraging implicit regularities to improve accuracy and adaptability over traditional root-based methods.

Contribution

The proposed method infers structure and labels data tokens by working backwards from content regions, offering a simpler and more effective alternative to existing tree-based approaches.

Findings

More accurate data extraction from semi-structured pages

Better detection of template changes in web pages

Simpler implementation compared to traditional methods

Abstract

We propose a new technique to infer the structure and extract the tokens of data from the semi-structured web sources which are generated using a consistent template or layout with some implicit regularities. The attributes are extracted and labeled reversely from the region of interest of targeted contents. This is in contrast with the existing techniques which always generate the trees from the root. We argue and show that our technique is simpler, more accurate and effective especially to detect the changes of the templates of targeted web pages.