Skip to main content
SpringerLink
Log in

Event Detection Based on Open Information Extraction and Ontology

  • Conference paper
  • First Online:
Book cover Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11683))

Included in the following conference series:

Abstract

Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) [15] has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. The named entity recognition and the reasoning which are automatic and based on techniques of adaptation and correspondence. Carried out experiments show the encouraging results of the main approaches of the literature.

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

Access this chapter

Log in via an institution
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 EPUB and 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

Notes

  1. 1.

    https://www.ontotext.com/knowledgehub/fundamentals/what-are-ontologies/.

  2. 2.

    https://protege.stanford.edu/.

  3. 3.

    The OLLIE tool is available at this address: https://github.com/knowitall/ollie.

  4. 4.

    https://www.ekino.com/articles/handson-de-quelques-taches-courantes-en-nlp.

References

  1. Grishman, R., Sundheim, B.: Message understanding conference-6: a brief history. In: Proceedings of the 16th Conference on Computational linguistics, pp. 466–471 (1996)

    Google Scholar 

  2. Talha, M., Boulaknadel, S., Aboutajdine, D.: Système de Reconnaissance des Entités Nommées Amazighes, In: actes de TALN, pp. 517–524 (2014)

    Google Scholar 

  3. Elloumi, S., Jaoua, A., et al.: General learning approach for event extraction: case of management change event. J. Inf. Sci. 39(2), 211–224 (2013)

    Article  Google Scholar 

  4. Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1535–1545 (2011)

    Google Scholar 

  5. Corro, L.D., Gemulla, R.: ClausIE: clause-based open information extraction. In: Proceedings of the 22nd Intlligence Conference on World Wide Web, pp. 355–366 (2013)

    Google Scholar 

  6. Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of the 20th IJCAI 2007, pp. 2670–2676 (2007)

    Google Scholar 

  7. Wu, F., Weld, D.S.: Open information extraction using wikipedia. In: Proceedings of the 48th Annual Meeting of ACL 2010, pp. 118–127 (2010)

    Google Scholar 

  8. Vo, D.-T., Bagheri, E.: Open Information Extraction, pp. 1–6 (2016)

    Google Scholar 

  9. Shaalan, K., Raza, H.: NERA: named entity recognition for arabic. J. Am. Soc. Inform. Sci. Technol. 60(8), 1652–1663 (2009)

    Article  Google Scholar 

  10. Benajiba, Y., Diab, M., Rosso, P.: Using language independent and language specific features to enhance arabic named entity recognition. Int. Arab J. Inf. Technol. 6(5), 464–473 (2009)

    Google Scholar 

  11. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics COLING 1992, pp. 539–545 (1992)

    Google Scholar 

  12. Berland, M., Charniak, E.: Finding parts in very large corpora. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, pp. 57–64 (1999)

    Google Scholar 

  13. Elloumi, S.: An adaptive model for sequential labeling systems. Multimedia Tools and Applications (2019). https://doi.org/10.1007/s11042-019-7558-8. ISSN: 1573-7721

    Article  Google Scholar 

  14. Sarawagi, S., Cohen, W.: Semi-Markov Conditional Random Fields for Information Extraction. pp. 1–8 (2004)

    Google Scholar 

  15. Serrano, L., et al.: Vers un système de capitalisation des connaissances: extraction d’événements par combinaison de plusieurs approches, pp. 1–14 (2012)

    Google Scholar 

  16. Kodelja, D., Besancon, R., Ferret, O.: Représentations et modèles en extraction d’événements supervisée, CEA, LIST, France, pp. 1–7 (2017)

    Google Scholar 

Download references

Acknowledgment

This work was made possible thanks to the Astra funding program Grant 2014-2020.4.01.16-032.

Author information

Authors and Affiliations

  1. University of Tunis El Manar, Faculty of Sciences of Tunis, LIPAH-LR11ES14, Tunis, Tunisia

    Sihem Sahnoun, Samir Elloumi & Sadok Ben Yahia

  2. Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

    Sadok Ben Yahia

Authors
  1. Sihem Sahnoun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samir Elloumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sadok Ben Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sihem Sahnoun , Samir Elloumi or Sadok Ben Yahia .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l’Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l’Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l’Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sahnoun, S., Elloumi, S., Yahia, S.B. (2019). Event Detection Based on Open Information Extraction and Ontology. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-28377-3_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28376-6

  • Online ISBN: 978-3-030-28377-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation