Skip to main content

Information Tracking Extraction for Emergency Scenario Response

  • Conference paper
  • First Online:
Intelligent Information Processing XI (IIP 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 643))

Included in the following conference series:

  • 680 Accesses

Abstract

With the frequent occurrences of emergencies, scenario response is proposed by researchers for emergency management. On the one hand, extracting emergency-related information is necessary and important for describing emergency scenario correctly; On the other hand, some emergency-related information evolve dynamically with time going on. To this end, a method for information extraction based on event tracking (called as information tracking extraction) for emergency scenario response is proposed. Based on the emergency tracking, it firstly judges the type of the emergency; secondly extracts time, location, casualty and loss based on multi strategies; finally adopts E-charts to visualize the extraction results. Experimental results show the proposed method can achieve high precision and recall, and the visualization of the tracking extraction results can display the dynamic evolution process better, which is very helpful to the scientific decision-making in the emergency management.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Shi, W., Wang, H.W., He, S.Y.: Sentiment analysis of Chinese microblogging based on sentiment ontology: a case study of ‘7.23 Wenzhou Train Collision.’ Conn. Sci. 25(4), 161–178 (2013)

    Article  Google Scholar 

  2. Tran, Q.K., Song, S.K.: Learning pattern of hurricane damage levels using semantic web resources. Int. J. Comput. Sci. Eng. 20(4), 492–500 (2019)

    Google Scholar 

  3. Guo, X.Y., He, T.T.: Survey about research on information extraction. Comput. Sci. 42(2), 14–17 (2015)

    Google Scholar 

  4. Brunner, D.: Advanced methods for building information extraction from very high resolution SAR data to support emergency response. Unpublished Ph.D. thesis, University of Trento, UniTrento, Italy (2009)

    Google Scholar 

  5. Amailef, K., Lu, J.: Mobile-based emergency response system using ontology-supported information extraction. In: Handbook on Decision, vol. 2, pp. 429–449 (2012). https://doi.org/10.1007/978-3-642-25755-1_21

  6. Chen, Z., Zheng, S.: Research on chemical emergency information extraction based on multi algorithm fusion. Comput. Digit. Eng. 46(2), 264–268 (2018)

    Google Scholar 

  7. Michele, B., Michael, J.C., Stephen, S., Matt, B., Oren, E.: Open information extraction from the web. In: Proceedings of the 2007 International Joint Conference on Artificial Intelligence, San Francisco, USA, pp. 2670–2676 (2007)

    Google Scholar 

  8. Jiang, D.L.: Research on extraction of emergency event information based on rules matching. Comput. Eng. Des. 31(14), 3294–3297 (2010)

    Google Scholar 

  9. Wang, N., Chen, Y., Guo, W., Zhong, Q.Y., Wang, Y.Z.: A method for emergency case information extraction based on knowledge element. Syst. Eng. 32(12), 133–139 (2014)

    Google Scholar 

  10. Zhao, H., Zhao, T.J., Yu, H., Zheng, D.Q.: English topic tracking research based on query vector. J. Comput. Res. Dev. 44(8), 1412–1417 (2007)

    Article  Google Scholar 

  11. Gu, Y.W., Wang, Y.R., Huan, J., Sun, Y.Q., Xu, S.K.: An improved TFIDF algorithm based on dual parallel adaptive computing model. Int. J. Embed. Syst. 13(1), 18–27 (2020)

    Article  Google Scholar 

  12. Li, X.O., Yu, W.: Imbalanced data classification via support vector machines and genetic algorithms Jair Cervantes. Conn. Sci. 26(4), 335–348 (2014)

    Article  Google Scholar 

  13. Wang, F.F., Liu, Z., Wang, C.D.: An improved kNN text classification method. Int. J. Comput. Sci. Eng. 20(3), 397–403 (2019)

    Google Scholar 

  14. Zhang, D.B., Shou, Y.F., Xu, J.M.: An improved parallel K-means algorithm based on MapReduce. Int. J. Embed. Syst. 9(3), 275–282 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Shandong Natural Science Foundation Project (No. ZR2021MG038); Special Study on Cultural Tourism of Shandong Social Science Planning (No. 21CLYJ32); 2019 Qingdao Philosophy and Social Science Planning Research Project (No. QDSKL1901124); Shandong Postgraduate Education Quality Improvement Plan (No. SDYJG19075); Shandong Education Teaching Research Key Project (No. 2021JXZ010); Qingdao City Philosophy and Social Science Planning Project (No. QDSKL2001128); National Statistical Science Research Project (No. 2021LY053) and Shandong University of Science and Technology Education and Teaching Research “Stars Plan” Project (No. QX2021M29).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hua Zhao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, H., Li, X., Zhang, P., Song, Z. (2022). Information Tracking Extraction for Emergency Scenario Response. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-03948-5_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-03947-8

  • Online ISBN: 978-3-031-03948-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics