Optimal schemes for robust web extraction

Author:

Parameswaran Aditya1,Dalvi Nilesh2,Garcia-Molina Hector1,Rastogi Rajeev3

Affiliation:

1. Stanford University

2. Yahoo! Research

3. Yahoo! Labs Bangalore

Abstract

In this paper, we consider the problem of constructing wrappers for web information extraction that are robust to changes in websites. We consider two models to study robustness formally: the adversarial model, where we look at the worst-case robustness of wrappers, and probabilistic model, where we look at the expected robustness of wrappers, as web-pages evolve. Under both models, we present efficient algorithms for constructing the provably most robust wrapper. By evaluating on real websites, we demonstrate that in practice, our algorithms are highly effective in coping up with changes in websites, and reduce the wrapper breakage by up to 500% over existing techniques.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

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1. Learning Transferable Node Representations for Attribute Extraction from Web Documents;Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining;2022-02-11

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3. Robust Web Data Extraction Based on Unsupervised Visual Validation;Intelligent Information and Database Systems;2019

4. Robust and Noise Resistant Wrapper Induction;Proceedings of the 2016 International Conference on Management of Data;2016-06-14

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