A Semantic Web Pragmatic Approach to Develop Clinical Ontologies, and Thus Semantic Interoperability, Based in HL7 v2.XML Messaging

  • David Mendes
  • Irene Rodrigues
Part of the Communications in Computer and Information Science book series (CCIS, volume 221)


The ISO/HL7 27931:2009 standard intends to establish a global interoperability framework for Healthcare applications. However, being a messaging related protocol, it lacks a semantic foundation for interoperability at a machine treatable level has intended through the Semantic Web. There is no alignment between the HL7 V2.xml message payloads and a meaning service like a suitable ontology. Careful application of Semantic Web tools and concepts can ease extremely the path to the fundamental concept of Shared Semantics. In this paper the Semantic Web and Artificial Intelligence tools and techniques that allow aligned ontology population are presented and their applicability discussed. We present the coverage of HL7 RIM inadequacy for ontology mapping and how to circumvent it, NLP techniques for semi automated ontology population and discuss the current trends about knowledge representation and reasoning that concur to the proposed achievement.


Latent Dirichlet Allocation Latent Semantic Analysis Unify Medical Language System Word Sense Disambiguation Biomedical Ontology 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Meystre, S.M., Savova, G.K., Kipper-Schuler, K.C., Hurdle, J.F.: Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008)Google Scholar
  2. 2.
    Smith, B., Brochhausen, M.: Establishing and Harmonizing Ontologies in an Interdisciplinary Health Care and Clinical Research Environment (2008) Google Scholar
  3. 3.
    Obo-Owl RESTful Conversion API,
  4. 4.
    Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J.-F., HuaData, L.: Mining in Healthcare and Biomedicine: A Survey of the Literature. Journal of Medical Systems (2010)Google Scholar
  5. 5.
    Spasic, I., Ananiadou, S., McNaught, J., Kumar, A.: Text mining and ontologies in Biomedicine: Making sense of raw text. Brief Bioinform. 6(3), 239–251 (2005)CrossRefGoogle Scholar
  6. 6.
    Hofmann, T.: Probabilistic latent semantic analysis. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1999) Google Scholar
  7. 7.
    Deerwester, S., Dumais, S., Landauer, T., Furnas, G., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  8. 8.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning, Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  9. 9.
    Jollife, I.T.: Principal component analysis. In: Everitt, B.S., Howell, D.C. (eds.) Encyclopedia of Statistics in Behavioral Science, pp. 1580–1584. John Wiley and Sons Ltd., New York (2005)Google Scholar
  10. 10.
    Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975)CrossRefzbMATHGoogle Scholar
  11. 11.
    Smith, B., Brochhausen, M.: Putting biomedical ontologies to work. Methods of Information in Medicine 49(2), 135–140 (2010), doi:10.3414/ME9302CrossRefGoogle Scholar
  12. 12.
    Demner-Fushman, D., Mork, J.G., Shooshan, S.E., Aronson, A.R.: UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text. Journal of Biomedical Informatics 43, 587–594 (2010), doi:10.1016/j.jbi.2010.02.005CrossRefGoogle Scholar
  13. 13.
    Liu, K., Hogan, W.R., Crowley, R.S.: Natural Language Processing methods and systems for biomedical ontology learning. Journal of Biomedical Informatics 44, 163–179 (2011), doi:10.1016/j.jbi.2010.07.006CrossRefGoogle Scholar
  14. 14.
    Rodrigues, J.M., Kumar, A., Bousquet, C.: Using the CEN / ISO Standard for Categorial Structure to Harmonize the Development of WHO International Terminologies. Medical Informatics (Icd), 255–260 (2009), doi:10.3233/978-1-60750-044-5-255Google Scholar
  15. 15.
    Batet, M., Sanchez, D., Valls, A.: An ontology-based measure to compute semantic similarity in biomedicine. Journal of Biomedical Informatics 44, 118–125 (2010), doi:10.1016/j.jbi.2010.09.002CrossRefGoogle Scholar
  16. 16.
    HL7 Health Level Seven ® International,
  17. 17.
    The Biomedical Research Integrated Domain Group,
  18. 18.
    Smith, B., Ceusters, W.: HL7 RIM: An Incoherent Standard. Medical Informatics, 133–138 (August 2006) Google Scholar
  19. 19.
    Kifer, M., Lausen, G., Wu, J.: Logical foundations of object-oriented and frame based languages. Journal of the ACM 42(4), 741–843 (1995), doi:10.1145/210332.210335MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Cimino, J.J.: High-quality, Standard, Controlled Healthcare Terminologies Come of Age. Methods of Information in Medicine 50(2), 101–104 (2011), retrieved Google Scholar
  21. 21.
    Navigli, R., Velardi, P.: Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Trans. Pattern Anal. Mach. Intel (PAMI) 27, 1075–1086 (2005)CrossRefGoogle Scholar
  22. 22.
    Poesio, M., Vieira, R., Teufel, S.: Resolving bridging references in unrestricted text. In: Proceedings of the ACL Workshop on Operational Factors in Robust Anaphora Resolution, pp. 1–6 (1997)Google Scholar
  23. 23.
    Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to co-reference resolution of noun phrases. Comput. Linguist. 27, 521–544 (2001)CrossRefGoogle Scholar
  24. 24.
    Ng, V., Cardie, C.: Improving machine learning approaches to co-reference resolution. In: Proceedings of the 40th Annual Meeting of the ACL. ACL, Philadelphia (2001)Google Scholar
  25. 25.
    Friedman, C., Borlawsky, T., Shagina, L., Xing, H.R., Lussier, Y.A.: Bio-ontology and text: bridging the modeling gap. Bioinformatics 22, 2421–2429 (2006)CrossRefGoogle Scholar
  26. 26.
    Cornet, R., De Keizer, N.F., Abu-Hanna, A.: A framework for characterizing terminological systems. Methods Inf. Med. 45, 253–266 (2006)Google Scholar
  27. 27.
    Guarino, N., Welty, C.: Identity, Unity, and Individuality: Towards a formal toolkit for ontological analysis. In: Horn, W. (ed.) Proceedings of ECAI 2000, pp. 219–223. IOS Press, Berlin (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • David Mendes
    • 1
  • Irene Rodrigues
    • 1
  1. 1.Universidade de ÉvoraPortugal

Personalised recommendations