[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

An Ontology Based for Drilling Report Classification

  • Conference paper
MICAI 2006: Advances in Artificial Intelligence (MICAI 2006)

Abstract

This paper presents an application of an ontology based system for automated text analysis using a sample of a drilling report to demonstrate how the methodology works. The methodology used here consists basically of organizing the knowledge related to the drilling process by elaborating the ontology of some typical problems. The whole process was carried out with the assistance of a drilling expert, and by also using software to collect the knowledge from the texts. Finally, a sample of drilling reports was used to test the system, evaluating its performance on automated text classification.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 122.00
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Miura, K., Guilherme, I.R., Morooka, C.K., Mendes, J.R.P.: Processing Technical Daily Reports in Offshore Petroleum Engineering – An Experience. Journal of Advanced Computational Intelligence and Intelligent Informatics 7(2), 223–228 (2003)

    Google Scholar 

  2. Morooka, C.K., Rocha, A.F., Miura, K., Alegre, L.: Offshore Well Completion Operational Knowledge Acquisition and Structuring. In: Eleventh International Offshore Mechanics and Arctic Engineering Conference (OMAE), ASME, Glasgow, Scotland (1993)

    Google Scholar 

  3. Rocha, A.F., Guilherme, I.R., Theoto, M., Miyadahira, A.M.K., Koizumi, M.S.: A neural Network for extracting Knowledge from Natural Language Data Bases. IEEE Transactions on Neural Network 3(5) (1992)

    Google Scholar 

  4. Faraco, C.E., Moura, F.M.: Língua e Literatura, Editora Ática (1996)

    Google Scholar 

  5. Orengo, V.M., Huyck, C.: A Stemming Algorithm for the Portuguese Language. In: Proceeding SPIRE, pp. 186–193 (2001)

    Google Scholar 

  6. Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guilherme, I.R., de Souza Serapião, A.B., Rabelo, C., Mendes, J.R.P. (2006). An Ontology Based for Drilling Report Classification. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_99

Download citation

  • DOI: https://doi.org/10.1007/11925231_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49026-5

  • Online ISBN: 978-3-540-49058-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics