Nothing Special   »   [go: up one dir, main page]

Skip to main content

More Informative Open Information Extraction via Simple Inference

  • Conference paper
Advances in Information Retrieval (ECIR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8416))

Included in the following conference series:

  • 3035 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Tran, T., Mika, P., Wang, H., Grobelnik, M.: Semsearch 2011: the 4th Semantic Search Workshop. In: WWW (2011)

    Google Scholar 

  2. Balog, K., Serdyukov, P., de Vries, A.P.: Overview of the TREC 2010 Entity Track. In: TREC (2010)

    Google Scholar 

  3. Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: EMNLP, pp. 1535–1545 (2011)

    Google Scholar 

  4. Bast, H., Bäurle, F., Buchhold, B., Haussmann, E.: Broccoli: Semantic full-text search at your fingertips. CoRR (2012)

    Google Scholar 

  5. Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: IJCAI 2007, pp. 2670–2676 (2007)

    Google Scholar 

  6. Mausam, S.M., Soderland, S., Bart, R., Etzioni, O.: Open language learning for information extraction. In: EMNLP-CoNLL, pp. 523–534 (2012)

    Google Scholar 

  7. Corro, L.D., Gemulla, R.: ClausIE: clause-based open information extraction. In: WWW, pp. 355–366 (2013)

    Google Scholar 

  8. Bast, H., Haussmann, E.: Open information extraction via contextual sentence decomposition. In: ICSC (2013)

    Google Scholar 

  9. Banko, M., Etzioni, O.: The tradeoffs between open and traditional relation extraction. In: ACL, pp. 28–36 (2008)

    Google Scholar 

  10. Schoenmackers, S., Etzioni, O., Weld, D.S.: Scaling textual inference to the web. In: EMNLP, pp. 79–88 (2008)

    Google Scholar 

  11. Lao, N., Mitchell, T.M., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: EMNLP, pp. 529–539 (2011)

    Google Scholar 

  12. Levy, R., Andrew, G.: Tregex and Tsurgeon: tools for querying and manipulating tree data structures. In: LREC, pp. 2231–2234 (2006)

    Google Scholar 

  13. Nakashole, N., Weikum, G., Suchanek, F.M.: PATTY: A taxonomy of relational patterns with semantic types. In: EMNLP-CoNLL, pp. 1135–1145 (2012)

    Google Scholar 

  14. Schoenmackers, S., Davis, J., Etzioni, O., Weld, D.S.: Learning first-order horn clauses from web text. In: EMNLP, pp. 1088–1098 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Bast, H., Haussmann, E. (2014). More Informative Open Information Extraction via Simple Inference. In: de Rijke, M., et al. Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham. https://doi.org/10.1007/978-3-319-06028-6_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06028-6_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06027-9

  • Online ISBN: 978-3-319-06028-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics