U-REST: an unsupervised record extraction system

In this paper, we describe a system that can extract record structures from web pages with no direct human supervision. Records are commonly occurring HTML-embedded data tuples that describe people, offered courses, products, company profiles etc. We present a simplified framework for studying the problem of unsupervised record extraction ? one which separates the algorithms from the feature engineering. Our system, U-REST formalizes an approach to the problem of unsupervised record extraction using a simple a two-stage machine learning framework. The first stage involves clustering, where structurally similar regions are discovered, and the second stage involves classification, where discovered groupings (clusters of regions) are ranked by their likelihood of being records. In our work, we describe, and summarize the results of an extensive survey of features for both stages. We conclude by comparing U-REST to related systems. The results of our empirical evaluation show encouraging ...

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Yuan Kui Shen, David R. Karger
• Keywords: Internet Technology, Keywords Record Extraction, Stage Involves Classification, Unsupervised Record Extraction, WWW 2007