Distantly Supervised Web Relation Extraction for Knowledge Base Population
Isabelle Augenstein and Diana Maynard and Fabio Ciravegna

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Bibtex:
@article{,
title= {Distantly Supervised Web Relation Extraction for Knowledge Base Population},
journal= {Semantic Web},
author= {Isabelle Augenstein and Diana Maynard and Fabio Ciravegna},
year= {2015},
url= {http://www.semantic-web-journal.net/content/distantly-supervised-web-relation-extraction-knowledge-base-population-0},
license= {},
abstract= {Extracting information from Web pages for populating large, cross-domain knowledge bases requires methods which are suitable across domains, do not require manual effort to adapt to new domains, are able to deal with noise, and integrate information extracted from different Web pages. Recent approaches have used existing knowledge bases to learn to extract information with promising results, one of those approaches being distant supervision. Distant supervision is an unsupervised method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. In this paper we propose the use of distant supervision for relation extraction from the Web. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains and extracting relations across sentence boundaries using unsupervised co- reference resolution methods. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. To combine information extracted from multiple sources for populating knowledge bases we present and evaluate several information integration strategies and show that those benefit immensely from additional relation mentions extracted using co-reference resolution, increasing precision by 8%. We further show that strategically selecting training data can increase precision by a further 3%.},
keywords= {},
terms= {}
}


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