Advertisement

A Biomedical Question Answering System Based on SNOMED-CT

  • Xinhua Zhu
  • Xuechen Yang
  • Hongchao Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Biomedical question answering system is an important research topic in biomedical natural language processing. To make full use of the semantic knowledge in SNOMED-CT for clinical medical service, we developed a biomedical question answering system based on SNOMED-CT, which has the following characteristics: (a) this system takes the semantic network in SNOMED-CT as a knowledge base to answer the clinical questions posed by physicians in natural language form, (b) a multi-layer nested structure of question templates is designed to map a template into the different semantic relationships in SNOMED-CT, (c) a template description logic system is designed to define the question templates and tag template elements so as to accurately represent question semantics, and (d) a textual entailment algorithm with semantics is proposed to match the question templates in order to consider both the flexibility and accuracy of the system. The experimental results show that the overall performance of the system has reached a high level, which can give 85% of the correct answer and be used as a biomedical question answering system in a real environment.

Keywords

Question answering system SNOMED-CT Template matching 

Notes

Acknowledgements

This work has been supported by the National Natural Science Foundation of China under the contract numbers 61462010 and 61363036, and Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.

References

  1. 1.
    Sun, C., Guan, Y., Wang, X., Wang, Q., Liu, T.: InsunTourQA: a restricted-domain question answering system. J. Comput. Inf. Syst. 3(4), 1581–1590 (2007)Google Scholar
  2. 2.
    Ely, J.W., Osheroff, J.A., Chambliss, M.L., Ebell, M.H., Rosenbaum, M.E.: Answering physicians’ clinical questions: obstacles and potential solutions. J. Am. Med. Inf. Assoc. 12(2), 217–224 (2005)CrossRefGoogle Scholar
  3. 3.
    Lee, M., Cimino, J., Zhu, H.R., Sable, C., Shanker, V., Ely, J., et al.: Beyond information retrieval–medical question answering. Amia. Annu. Symp. Proc. 2006, 469–473 (2006)Google Scholar
  4. 4.
    Cao, Y., Liu, F., Simpson, P., Antieau, L., Bennett, A., Cimino, J.J., et al.: Askhermes: an online question answering system for complex clinical questions. J. Biomed. Inf. 44(2), 277–288 (2011)CrossRefGoogle Scholar
  5. 5.
    Cairns, B.L., Nielsen, R.D., Masanz, J.J., Martin, J.H., Palmer, M.S., Ward, W.H., et al.: The MiPACQ clinical question answering system. AMIA. Annu. Symp. Proc. 2011, 171–180 (2011)Google Scholar
  6. 6.
    Abacha, A.B., Zweigenbaum, P.: MEANS: a medical question-answering system combining NLP techniques and semantic web technologies. Inf. Process. Manag. 51(5), 570–594 (2015)CrossRefGoogle Scholar
  7. 7.
    Ray, S.K., Singh, S., Joshi, B.P., Beach, J.E.: A semantic approach for question classification using wordnet and Wikipedia. Pattern Recogn. Lett. 31(13), 1935–1943 (2010)CrossRefGoogle Scholar
  8. 8.
    Popescu, A.M., Etzioni, O., Kautz, H.: Towards a theory of natural language interfaces to databases. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 149–157. ACM New York (2003)Google Scholar
  9. 9.
    Wong, W., Thangarajah, J., Padgham, L.: Contextual question answering for the health domain. J. Am. Soc. Inf. Sci. Technol. 63(11), 2313–2327 (2012)CrossRefGoogle Scholar
  10. 10.
    Asiaee, A.H., Minning, T., Doshi, P., Tarleton, R.L.: A framework for ontology-based question answering with application to parasite immunology. J. Biomed. Semant. 6(1), 31–56 (2015)CrossRefGoogle Scholar
  11. 11.
    Baader, F., Calvanese, D., Mcguinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation and Applications, 2nd edn. Cambridge University Press, New York (2010)MATHGoogle Scholar
  12. 12.
    Strassner, J.: Handbook of Network and System Administration: Knowledge Engineering Using Ontologies. Elsevier, Amsterdam (2008)Google Scholar
  13. 13.
    Harispe, S., Sãnchez, D., Ranwez, S., Janaqi, S., Montmain, J.: A framework for unifying ontology-based semantic similarity measures: a study in the biomedical domain. J. Biomed. Inform. 48(2), 38–53 (2014)CrossRefGoogle Scholar
  14. 14.
    Humphreys, B.L., Lindberg, D.A.: The UMLS project: making the conceptual connection between users and the information they need. Bull. Med. Libr. Assoc. 81(2), 170 (1993)Google Scholar
  15. 15.
    Wei, D., Helen, G.H., Perl, Y., Halper, M., Ochs, C., Elhanan, G., et al.: Structural measures to track the evolution of SNOMED CT hierarchies. J. Biomed. Inf. 57(C), 278–287 (2015)CrossRefGoogle Scholar
  16. 16.
    Zhu, X., Li, F., Chen, H., Peng, Q.: An efficient path computing model for measuring semantic similarity using edge and density. Knowl. Inf. Syst. 55(1), 79–111 (2018)CrossRefGoogle Scholar
  17. 17.
    Kim, H.Y., Park, H.A.: Development and evaluation of data entry templates based on the entity-attribute-value model for clinical decision support of pressure ulcer wound management. Int. J. Med. Inf. 81(7), 485–492 (2012)CrossRefGoogle Scholar
  18. 18.
    Liu, J., Lane, K., Lo, E., Lam, M., Truong, T., Veillette, C.: Addressing SNOMED CT implementation challenges through multi-disciplinary collaboration. Stud. Health Technol. Inf. 160(2), 981–985 (2010)Google Scholar
  19. 19.
    SNOMED CT Technical Implementation Guide January 2015 International Release (US English). https://confluence.ihtsdotools.org/display/DOC
  20. 20.
    Miller, G.A., Fellbaum, C.: Semantic networks of english. Int. J. Cogn. Sci. 41(1), 197–229 (1991)Google Scholar
  21. 21.
    Lopez, V., Uren, V., Motta, E., Pasin, M.: AquaLog: an ontology-driven question answering system for organizational semantic intranets. J. Web Semant. 5(2), 72–105 (2007)CrossRefGoogle Scholar
  22. 22.
    Dzikovska, M., Steinhauser, N., Farrow, E., Moore, J., Campbell, G.: BEETLE II: deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics. Int. J. AIED. 24(3), 284–332 (2014)Google Scholar
  23. 23.
    Zhu, X.H., Cao, Q.H., Su, F.F.: A chinese intelligent question answering system based on domain ontology and sentence templates. Int. J. Digit. Content Technol. Appl. 5(11), 158–165 (2011)CrossRefGoogle Scholar
  24. 24.
    Wang, D.: Answering contextual questions based on ontologies and question templates. Front. Comput. Sci-Chi. 5(4), 405–418 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
  26. 26.
    Mccallum, A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp. 188–191. ACL, Stroudsburg (2003)Google Scholar
  27. 27.
    Bos, J., Markert, K.: Recognising Textual entailment with robust logical inference. In: Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 628–635. ACL, Stroudsburg (2006)Google Scholar
  28. 28.
    Ferrández, Ó., Micol, D., Muñoz, R., Palomar, M.: DLSITE-1: lexical analysis for solving textual entailment recognition. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds.) NLDB 2007. LNCS, vol. 4592, pp. 284–294. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-73351-5_25CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Lab of Multi-Source Information Mining and Security, College of Computer Science and Information EngineeringGuangxi Normal UniversityGuilinChina

Personalised recommendations