More Informative Open Information Extraction via Simple Inference

  • Hannah Bast
  • Elmar Haussmann
Conference paper

DOI: 10.1007/978-3-319-06028-6_61

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)
Cite this paper as:
Bast H., Haussmann E. (2014) More Informative Open Information Extraction via Simple Inference. In: de Rijke M. et al. (eds) Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham

Abstract

Recent Open Information Extraction (OpenIE) systems utilize grammatical structure to extract facts with very high recall and good precision. In this paper, we point out that a significant fraction of the extracted facts is, however, not informative. For example, for the sentence The ICRW is a non-profit organization headquartered in Washington, the extracted fact (a non-profit organization)(is headquartered in)(Washington) is not informative. This is a problem for semantic search applications utilizing these triples, which is hard to fix once the triple extraction is completed. We therefore propose to integrate a set of simple inference rules into the extraction process. Our evaluation shows that, even with these simple rules, the percentage of informative triples can be improved considerably and the already high recall can be improved even further. Both improvements directly increase the quality of search on these triples.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hannah Bast
    • 1
  • Elmar Haussmann
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany

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