Semi-automated Ontology Development and Management System Applied to Medically Unexplained Syndromes in the U.S. Veterans Population

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Terminologies or ontologies to describe patient-reported information are lacking. The development and maintenance of ontologies is usually a manual, lengthy, and resource-intensive process. To support the development of medical specialty-specific ontologies, we created a semi-automated ontology development and management system (SEAM). SEAM supports ontology development by automatically extracting terms, concepts, and relations from narrative text, and then offering a streamlined graphical user interface to edit and create content in the ontology and finally export it in OWL format. The graphical user interface implements card sorting for synonym grouping and concept laddering for hierarchy construction. We used SEAM to create ontologies to support medically unexplained syndromes detection and management among veterans in the U.S.

Keywords

Ontology Terminology Natural language processing 

References

  1. 1.
    Gruber T.: A translation approach to portable ontology specifications. Knowledge Acquisition Stanford, CA, Technical report KSL, vol. 5(2), pp. 199–220 (1993)Google Scholar
  2. 2.
    Doing-Harris, K., Meystre, S.M., Samore, M., Ceusters, W.: Applying ontological realism to medically unexplained syndromes. Stud. Health Technol. Inform. 192, 97–101 (2013)Google Scholar
  3. 3.
  4. 4.
    Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005). doi:10.1007/11428817_21 CrossRefGoogle Scholar
  5. 5.
    Faure, D., Nédellec, C.: A corpus-based conceptual clustering method for verb frames and ontology acquisition. In: LREC workshop on adapting lexical and corpus resources to sublanguages and applications, pp. 707–728 (1998)Google Scholar
  6. 6.
    Wang, Y., Sure, Y., Stevens, R., Rector, A.: Knowledge elicitation plug-in for protege: card sorting and laddering. In: Mizoguchi, R., Shi, Z., Giunchiglia, F. (eds.) ASWC 2006. LNCS, vol. 4185, pp. 552–565. Springer, Heidelberg (2006). doi:10.1007/11836025_53 CrossRefGoogle Scholar
  7. 7.
    Upchurch, L., Rugg, G., Kitchenham, B.: Using card sorts to elicit web page quality attributes. IEEE Softw. 18(4), 84–89 (2001)CrossRefGoogle Scholar
  8. 8.
    Shadbolt, N., O’hara, K., Crow, L.: The experimental evaluation of knowledge acquisition techniques and methods: history, problems and new directions. Int. J. Hum.-Comput. Stud. 51(4), 729–755 (1999)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Völker, J., Haase, P.: Towards semi-automatic ontology building supported by large-scale knowledge acquisition. In: AAAI Fall Symposium on Semantic Web for Collaborative Knowledge Acquisition, vol. 6, p. 06 (2006)Google Scholar
  10. 10.
    Doing-Harris, K., Livnat, Y., Meystre, S.: Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system. J. Biomed. Semant. 6(1), 15 (2015)CrossRefGoogle Scholar
  11. 11.
    Doing-Harris, K., Boonsirisumpun, N., Potter, K., Livnat, Y., Meystre, S.M.: Automated concept and relationship extraction for ontology development. In: AMIA, p. 344 (2013)Google Scholar
  12. 12.
    Eclipse RDF4 J. http://rdf4j.org
  13. 13.
  14. 14.
    World Health Organization. ICD-11 Revision. http://www.who.int/classifications/icd/revision/en/
  15. 15.
    Uzuner, O., Solti, I., Xia, F., Cadag, E.: Community annotation experiment for ground truth generation for the i2b2 medication challenge. J. Am. Med. Inf. Assoc. 17(5), 519–523 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Medical University of South CarolinaCharlestonUSA
  2. 2.Westminster CollegeSalt Lake CityUSA

Personalised recommendations