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Adapting semantic natural language processing technology to address information overload in influenza epidemic management

Author

Listed:
  • Alla Keselman
  • Graciela Rosemblat
  • Halil Kilicoglu
  • Marcelo Fiszman
  • Honglan Jin
  • Dongwook Shin
  • Thomas C. Rindflesch

Abstract

The explosion of disaster health information results in information overload among response professionals. The objective of this project was to determine the feasibility of applying semantic natural language processing (NLP) technology to addressing this overload. The project characterizes concepts and relationships commonly used in disaster health‐related documents on influenza pandemics, as the basis for adapting an existing semantic summarizer to the domain. Methods include human review and semantic NLP analysis of a set of relevant documents. This is followed by a pilot test in which two information specialists use the adapted application for a realistic information‐seeking task. According to the results, the ontology of influenza epidemics management can be described via a manageable number of semantic relationships that involve concepts from a limited number of semantic types. Test users demonstrate several ways to engage with the application to obtain useful information. This suggests that existing semantic NLP algorithms can be adapted to support information summarization and visualization in influenza epidemics and other disaster health areas. However, additional research is needed in the areas of terminology development (as many relevant relationships and terms are not part of existing standardized vocabularies), NLP, and user interface design.

Suggested Citation

  • Alla Keselman & Graciela Rosemblat & Halil Kilicoglu & Marcelo Fiszman & Honglan Jin & Dongwook Shin & Thomas C. Rindflesch, 2010. "Adapting semantic natural language processing technology to address information overload in influenza epidemic management," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2531-2543, December.
    • Handle: RePEc:bla:jamist:v:61:y:2010:i:12:p:2531-2543
    DOI: 10.1002/asi.21414
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    Cited by:

    1. Xiaoying Li & Suyuan Peng & Jian Du, 2021. "Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6225-6251, July.
    2. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.

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