Extracting structured data from Web pages

TLDR

Many websites generate pages from a common template, with database values such as author or title consistently placed across pages, as seen on Amazon. The study aims to automatically extract database values from template‑generated web pages without relying on training data or human guidance. They formalize a template model and present an algorithm that infers the unknown template from a set of pages and extracts the encoded values. Experiments on numerous real page collections show the algorithm correctly extracts data in most cases.

Abstract

Many web sites contain large sets of pages generated using a common template or layout. For example, Amazon lays out the author, title, comments, etc. in the same way in all its book pages. The values used to generate the pages (e.g., the author, title,...) typically come from a database. In this paper, we study the problem of automatically extracting the database values from such template-generated web pages without any learning examples or other similar human input. We formally define a template, and propose a model that describes how values are encoded into pages using a template. We present an algorithm that takes, as input, a set of template-generated pages, deduces the unknown template used to generate the pages, and extracts, as output, the values encoded in the pages. Experimental evaluation on a large number of real input page collections indicates that our algorithm correctly extracts data in most cases.