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Extending SemRep to the public health domain

Author

Listed:
  • Graciela Rosemblat
  • Melissa P. Resnick
  • Ione Auston
  • Dongwook Shin
  • Charles Sneiderman
  • Marcelo Fizsman
  • Thomas C. Rindflesch

Abstract

We describe the use of a domain‐independent method to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®; Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information‐savvy workforce. Natural language processing and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user‐friendly visualization application as a way to summarize results of PubMed® searches in this field of knowledge.

Suggested Citation

  • Graciela Rosemblat & Melissa P. Resnick & Ione Auston & Dongwook Shin & Charles Sneiderman & Marcelo Fizsman & Thomas C. Rindflesch, 2013. "Extending SemRep to the public health domain," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(10), pages 1963-1974, October.
    • Handle: RePEc:bla:jamist:v:64:y:2013:i:10:p:1963-1974
    DOI: 10.1002/asi.22899
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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.

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