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Semantic Rule Filtering for Web-Scale Relation Extraction

  • Andrea Moro
  • Hong Li
  • Sebastian Krause
  • Feiyu Xu
  • Roberto Navigli
  • Hans Uszkoreit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8218)

Abstract

Web-scale relation extraction is a means for building and extending large repositories of formalized knowledge. This type of automated knowledge building requires a decent level of precision, which is hard to achieve with automatically acquired rule sets learned from unlabeled data by means of distant or minimal supervision. This paper shows how precision of relation extraction can be considerably improved by employing a wide-coverage, general-purpose lexical semantic network, i.e., BabelNet, for effective semantic rule filtering. We apply Word Sense Disambiguation to the content words of the automatically extracted rules. As a result a set of relation-specific relevant concepts is obtained, and each of these concepts is then used to represent the structured semantics of the corresponding relation. The resulting relation-specific subgraphs of BabelNet are used as semantic filters for estimating the adequacy of the extracted rules. For the seven semantic relations tested here, the semantic filter consistently yields a higher precision at any relative recall value in the high-recall range.

Keywords

Relation Extraction Semantics WSD Rule Filtering Web-scale Semantic relations 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrea Moro
    • 1
  • Hong Li
    • 2
  • Sebastian Krause
    • 2
  • Feiyu Xu
    • 2
  • Roberto Navigli
    • 1
  • Hans Uszkoreit
    • 2
  1. 1.Dipartimento di InformaticaSapienza Università di RomaRomaItaly
  2. 2.Language Technology LabDFKIBerlinGermany

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