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Hierarchical Wrapper Induction for Semistructured Information Sources (2001)
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| Venue: | Journal of Autonomous Agents and Multi-Agent Systems |
| Citations: | 185 - 34 self |
BibTeX
@ARTICLE{Muslea01hierarchicalwrapper,
author = {Ion Muslea and Steven Minton and Craig A. Knoblock},
title = {Hierarchical Wrapper Induction for Semistructured Information Sources},
journal = {Journal of Autonomous Agents and Multi-Agent Systems},
year = {2001},
volume = {4},
pages = {93--114}
}
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Abstract
With the tremendous amount of information that becomes available on the Web on a daily basis, the ability to quickly develop information agents has become a crucial problem. A vital component of any Webbased information agent is a set of wrappers that can extract the relevant data from semistructured information sources. Our novel approach to wrapper induction is based on the idea of hierarchical information extraction, which turns the hard problem of extracting data from an arbitrarily complex document into a series of simpler extraction tasks. We introduce an inductive algorithm, stalker, that generates high accuracy extraction rules based on userlabeled training examples. Labeling the training data represents the major bottleneck in using wrapper induction techniques, and our experimental results show that stalker requires up to two orders of magnitude fewer examples than other algorithms. Furthermore, stalker can wrap information sources that could not be wrapped by existing inductive techniques
Keyphrases
semistructured information source hierarchical wrapper induction information source information agent novel approach simpler extraction task tremendous amount hard problem complex document high accuracy extraction rule relevant data crucial problem inductive technique training data major bottleneck training example experimental result inductive algorithm wrapper induction technique vital component daily basis hierarchical information extraction
