Mobile-Based Emergency Response System Using Ontology-Supported Information Extraction

  • Khaled Amailef
  • Jie Lu
Part of the Intelligent Systems Reference Library book series (ISRL, volume 33)

Abstract

This chapter describes an algorithm within a Mobile-based Emergency Response System (MERS) to automatically extract information from Short Message Service (SMS). The algorithm is based on an ontology concept, and a maximum entropy statistical model. Ontology has been used to improve the performance of an information extraction system. A maximum entropy statistical model with various predefined features offers a clean way to estimate the probability of certain token occurring with a certain SMS text. The algorithm has four main functions: to collect unstructured information from an SMS emergency text message; to conduct information extraction and aggregation; to calculate the similarity of SMS text messages; and to generate query and results presentation.

Keywords

Mobile User Text Message Information Extraction Disaster Event Name Entity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Moon, M.J.: From e-government to m-government? Report, IBM Center for The Business of Government, Washington (2004)Google Scholar
  2. 2.
    Huijnen, C.: Mobile tourism and mobile government - an inventory of european projects. Working paper, European Center for Digital Communications (2006)Google Scholar
  3. 3.
    Careem, M., et al.: Demonstration of sahana: Free and open source disaster management. In: Proceedings of the 8th Annual International Conference on Digital Government Research: Bridging Disciplines & Domains, Philadelphia, PA (2007)Google Scholar
  4. 4.
    NCRIS, 2008 strategic roadmap for australian research infrastructure, Department of Innovation, Industry, Science and Research (DIISR), Canberra (2008)Google Scholar
  5. 5.
    Jaeger, P.T., et al.: Community response grids: E-government, social networks, and effective emergency management. Telecommun. Policy 31, 592–604 (2007)CrossRefGoogle Scholar
  6. 6.
    Kim, S., et al.: Mobile analytics for emergency response and training. Information Visualization, Houndmills 7, 77–88 (2008)CrossRefGoogle Scholar
  7. 7.
    Rossel, P., et al.: “Mobile” e-government options: Between technology-driven and usercentric. The Electronic Journal of e-Government 4, 79–86 (2006)Google Scholar
  8. 8.
    El-Kiki, T., Lawrence, E.: Government as a mobile enterprise: Real-time, ubiquitous government. In: Proceedings of 3rd International Conference on Information Technology: New Generations, Las Vegas, Nevada, pp. 320–327 (2006)Google Scholar
  9. 9.
    Kim, Y., et al.: Architecture for implementing the mobile government services in korea. In: First International Workshop on Digital Government: Systems and Technologies (DGOV 2004). LNCS, pp. 601–614 (2004)Google Scholar
  10. 10.
    Saldhana, A.: Secure e-government portals - building a web of trust and convenience for global citizens. Presented at the Paper Presented to the W3C Workshop on e-Government and the Web, Washington DC, USA (2007)Google Scholar
  11. 11.
    Wang, H., Rong, Y.: Case based reasoning method for computer aided welding fixture design. Computer-Aided Design 40, 1121–1132 (2008)CrossRefGoogle Scholar
  12. 12.
    Amailef, K., Lu, J.: M-government: A framework of mobile-based emergency response system. Presented at the The International Conference on Intelligent System and Knowledge Engineering, Xiamen, China (2008)Google Scholar
  13. 13.
    Antovski, L., Gusev, M.: M-government framework. In: EURO mGov 2005, pp. 36–44. University of Sussex, Brighton (2005)Google Scholar
  14. 14.
    Kushchu, I., Kuscu, H.: From e-government to m-government: Facing the inevitable. In: Proceedings of European Conference on E-Governemnt (ECEG 2003), Trinity College, Dublin (2003)Google Scholar
  15. 15.
    El-Kiki, T., et al.: A management framework for mobile government services. In: Proceedings of CollECTeR, Sydney, Australia (2005)Google Scholar
  16. 16.
    Yildiz, B., Miksch, S.: OntoX - A Method for Ontology-Driven Information Extraction. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part III. LNCS, vol. 4707, pp. 660–673. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Soysal, E., et al.: Design and evaluation of an ontology based information extraction system for radiological reports. Computers in Biology and Medicine 40, 900–911 (2010)CrossRefGoogle Scholar
  18. 18.
    Zhou, G., et al.: Tree kernel-based semantic relation extraction with rich syntactic and semantic information. Information Sciences 180(8), 1313–1325 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Roman, Y., et al.: Extracting information about outbreaks of infectious epidemics. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, Vancouver, BC, Canada (2005)Google Scholar
  20. 20.
    Moens, M.-F.: Information extraction: algorithms and prospects in a retrieval context. Springer (2006)Google Scholar
  21. 21.
    Bahora, A.S., et al.: Integrated peer-to-peer applications for advanced emergency response systems. Part ii. Technical feasibility. In: Systems and Information Engineering Design Symposium, pp. 261–268. IEEE (2003)Google Scholar
  22. 22.
    Zhu, H., et al.: Information aggregation – a value-added e-service. In: Proceedings of the International Conference on Technology, Policy, and Innovation: Critical Infrastructures, The Netherlands (2001)Google Scholar
  23. 23.
    Saha, S.K., et al.: A composite kernel for named entity recognition. Pattern Recognition Letters 31(12), 1591–1597 (2010)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Li, L., et al.: Two-phase biomedical named entity recognition using crfs. Computational Biology and Chemistry 33, 334–338 (2009)CrossRefGoogle Scholar
  25. 25.
    Yang, Z., et al.: Exploiting the contextual cues for bio-entity name recognition in biomedical literature. Journal of Biomedical Informatics 41(4), 580–587 (2008)CrossRefGoogle Scholar
  26. 26.
    Min-Kyoung, K., Han-Joon, K.: Design of question answering system with automated question generation. In: Proceedings of the 4th International Conference on Networked Computing and Advanced Information Management, vol. 02, pp. 365–368 (2008)Google Scholar
  27. 27.
    Valentin, J., et al.: Named entity normalization in user generated content. In: Proceedings of the 2nd Workshop on Analytics for Noisy Unstructured Text Data, Singapore (2008)Google Scholar
  28. 28.
    Hai Leong, C., Hwee Tou, N.: Named entity recognition with a maximum entropy approach. In: Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL, Edmonton, Canada, vol. 4, pp. 160–163 (2003)Google Scholar
  29. 29.
    Kozareva, Z., Ferrández, Ó., Montoyo, A., Muńoz, R., Suárez, A.: Combining Data-Driven Systems for Improving Named Entity Recognition. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 80–90. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  30. 30.
    Shaojun, Z.: Named entity recognition in biomedical texts using an hmm model. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications, Geneva, Switzerland, pp. 84–87 (2004)Google Scholar
  31. 31.
    Saha, S.K., et al.: Feature selection techniques for maximum entropy based biomedical named entity recognition. Journal of Biomedical Informatics 42(5), 905–911 (2009)CrossRefGoogle Scholar
  32. 32.
    Sun, C., et al.: Rich features based conditional random fields for biological named entities recognition. Computers in Biology and Medicine 37(9), 1327–1333 (2007)CrossRefGoogle Scholar
  33. 33.
    Takeuchi, K., Collier, N.: Bio-medical entity extraction using support vector machines. Artificial Intelligence in Medicine 33(2), 125–137 (2005)CrossRefGoogle Scholar
  34. 34.
    Javed, A.A., et al.: The maximum entropy method for analyzing retrieval measures. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil (2005)Google Scholar
  35. 35.
    Kumar, S.S., et al.: Feature selection techniques for maximum entropy based biomedical named entity recognition. Journal of Biomedical Informatics 42, 905–911 (2009)CrossRefGoogle Scholar
  36. 36.
    Tran Quoc, D., Kameyama, W.: A proposal of ontology-based health care information extraction system: Vnhies. In: 2007 IEEE International Conference on Research, Innovation and Vision for the Future, pp. 1-7 (2007)Google Scholar
  37. 37.
    Katharina, K., et al.: How can information extraction ease formalizing treatment processes in clinical practice guidelines? Artif. Intell. Med. 39, 151–163 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Khaled Amailef
    • 1
  • Jie Lu
    • 1
  1. 1.Decision Systems & e-Service Intelligence (DeSI) Lab, Centre for Quantum Computation & Intelligent Systems (QCIS), School of Software, Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyAustralia

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