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International Conference on Knowledge Engineering and Knowledge Management

EKAW 2014: Knowledge Engineering and Knowledge Management pp 26–41Cite as

Relation Extraction from the Web Using Distant Supervision

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8876))

Abstract

Extracting information from Web pages requires the ability to work at Web scale in terms of the number of documents, the number of domains and domain complexity. Recent approaches have used existing knowledge bases to learn to extract information with promising results. In this paper we propose the use of distant supervision for relation extraction from the Web. Distant supervision is a method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. 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, as well as extracting relations across sentence boundaries. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. Our experiments show that using a more robust entity recognition approach and expanding the scope of relation extraction results in about 8 times the number of extractions, and that strategically selecting training data can result in an error reduction of about 30%.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Sheffield, UK

    Isabelle Augenstein, Diana Maynard & Fabio Ciravegna

Authors
  1. Isabelle Augenstein
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  2. Diana Maynard
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  3. Fabio Ciravegna
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Editor information

Editors and Affiliations

  1. Department of Geography, University of California, Santa Barbara, CA, USA

    Krzysztof Janowicz

  2. Dept. of Computer Science, VU University Amsterdam, The Netherlands

    Stefan Schlobach

  3. University of Linköping, Sweden

    Patrick Lambrix

  4. Aalto University, Finland

    Eero Hyvönen

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© 2014 Springer International Publishing Switzerland

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Augenstein, I., Maynard, D., Ciravegna, F. (2014). Relation Extraction from the Web Using Distant Supervision. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds) Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8876. Springer, Cham. https://doi.org/10.1007/978-3-319-13704-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-13704-9_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13703-2

  • Online ISBN: 978-3-319-13704-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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