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Incorporating Semantics into Data Driven Workflows for Content Based Analysis

  • M. Argüello
  • M.J. Fernandez-Prieto
Conference paper

Abstract

Finding meaningful associations between text elements and knowledge structures within clinical narratives in a highly verbal domain, such as psychiatry, is a challenging goal. The research presented here uses a small corpus of case histories and brings into play pre-existing knowledge, and therefore, complements other approaches that use large corpus (millions of words) and no pre-existing knowledge. The paper describes a variety of experiments for content-based analysis: Linguistic Analysis using NLP-oriented approaches, Sentiment Analysis, and Semantically Meaningful Analysis. Although it is not standard practice, the paper advocates providing automatic support to annotate the functionality as well as the data for each experiment by performing semantic annotation that uses OWL and OWL-S. Lessons learnt can be transmitted to legacy clinical databases facing the conversion of clinical narratives according to prominent Electronic Health Records standards.

Keywords

Service Composition Sentiment Analysis Semantic Annotation Text Element Lexical Resource 
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.

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References

  1. 1.
    Sharda, P., Das, A.K., Patel, V.L.: Specifying design criteria for electronic medical record interface using cognitive framework. In: AMIA annual symposium, pp. 594-598 (2003).Google Scholar
  2. 2.
    Cohen, T., Blatter, B., Patel, V.: Simulating expert clinical comprehension: Adapting latent semantic analysis to accurately extract clinical concepts from psychiatric narrative. Journal of Biomedical Informatics 41, pp. 1070-1087 (2008).CrossRefGoogle Scholar
  3. 3.
    Doherty, G, Coyle, D., Matthews, M.: Design and evaluation guidelines for mental health technologies. Interacting with Computers 22, pp. 243-252 (2010).CrossRefGoogle Scholar
  4. 4.
    Berkley, C., Bowers, S., Jones, M.B., Ludascher, B., Schildhauer, M., Tao, J.: Incorporating Semantics in Scientific Workflow Authoring. 17th International Conference on Scientific and Statistical Database Management. IEEE Computer Society (2005).Google Scholar
  5. 5.
    myExperiment, http://www.myexperiment.org/. Accessed May 2010.
  6. 6.
  7. 7.
    openEHR Community, http://www.openehr.org/. Accessed Nov 2009.
  8. 8.
  9. 9.
    Zhou, L.: Ontology learning: state of the art and open issues. Information Technology and Management 8, pp. 241-252 (2007).CrossRefGoogle Scholar
  10. 10.
    Gacitua, R., Sawyer, P., Rayson, P.: A flexible framework to experiment with ontology learning techniques. Knowledge-Based System 21(3), pp. 192-199 (2008).CrossRefGoogle Scholar
  11. 11.
    Text2Onto, http://ontoware.org/projects/text2onto/. Accessed May 2010.
  12. 12.
    OWL, http://www.w3.org/2004/OWL/. Accessed May 2010.
  13. 13.
    Arguello, M., Gacitua, R., Osborne, J., Peters, S., Ekin, P., Sawyer, P.: Skeletons and Semantic Web descriptions to integrate Parallel Programming into Ontology Learning Frameworks. 11th International Conference on Computer Modelling and Simulation (2009).Google Scholar
  14. 14.
    Klein, E., Potter, S.: An ontology for NLP services. In Thierry Declerck ed., Proceedings of conference on Language Resources and Evaluation LREC’04 (2004).Google Scholar
  15. 15.
    OWL-S, http://www.w3.org/Submission/OWL-S/. Accessed May 2010.
  16. 16.
    Ilsey, J.E., Moffoot, A.P.R., O’Carroll, R.E.: An analysis of memory dysfunction in major depression. Journal of Affective Disorders 35, pp. 1-9 (1995).CrossRefGoogle Scholar
  17. 17.
    Fossati, P., Guillaume, L.B., Ergis, A.M., Allilaire, J.F.: Qualitative analysis of verbal fluency in depression. Psychiatry Research 17, pp. 17-24 (2003).CrossRefGoogle Scholar
  18. 18.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354 (2005).Google Scholar
  19. 19.
    General Inquirer, http://www.wjh.harvard.edu/~inquirer. Accessed May 2010.
  20. 20.
    Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available Lexical Resource for Opinion Mining. 5th Conference on Language Resources and Evaluation (LREC’06) (2006).Google Scholar
  21. 21.
    OpinionFinder’s Subjectivity Lexicon, http://www.cs.pitt.edu/mpqa, Accessed May 2010.
  22. 22.
    Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21(4), pp. 315-346 (2003).CrossRefGoogle Scholar
  23. 23.
    Moilanen, K., Pulman, S.: Sentiment composition. International Conference of Recent Advances in Natural Language Processing, pp. 378-382 (2007).Google Scholar
  24. 24.
    Prabowo, R., Thelwall, M.: Sentimen Analysis: A combined approach. Journal of Infometrics 3, pp. 143-157 (2009).CrossRefGoogle Scholar
  25. 25.
    Ryan, G.W., Bernard, H.R.: Data management and analysis methods. In: NK Denzin, YS Lincoln Eds. Handbook of Qualitative Research, Sage publications Inc, pp. 768-802 (2007).Google Scholar
  26. 26.
    Galen Ontology, http://www.co-ode.org/galen/. Accessed May 2010.
  27. 27.
    Open Biomedical Ontologies, http://www.obofoundry.org/. Accessed May 2010.
  28. 28.
    SNOMED CT, http://www.ihtsdo.org/our-standards/. Accessed May 2010.
  29. 29.
  30. 30.
    MedLinePlus, http://www.nlm.nih.gov/medlineplus/. Accessed May 2010.
  31. 31.
    UMLSKS, https://login.nlm.nih.gov/cas/. Accessed May 2010.
  32. 32.
    Thesaurus of Psychological Index Terms, American Psychological Association, Lisa A. Gallagher ed., 10th ed. (2004).Google Scholar
  33. 33.
    Fensel, D., Horrocks, I., van Harmelen, F., McGuinness, D.L., Patel-Schneider, P.: OIL: Ontology Infrastructure to Enable the Semantic Web. IEEE Intelligent Systems 16(2), pp. 38-45 (2001).CrossRefGoogle Scholar
  34. 34.
    Talantikite, H.N., Aissani, D., Boudjlida, N.: Semantic Annotations for web services discovery and composition, Computer Standards & Interfaces 31, pp. 1008-117 (2009).CrossRefGoogle Scholar
  35. 35.
    Habala, O., Paralic, M., Rozinajova, V., Bartalos, P.: Semantically-Aided Data-Aware Service Workflow Composition. In: SOFSEM, NLCS 5404, pp. 317-328 (2009).Google Scholar
  36. 36.
    Kepler, https://kepler-project.org/. Accessed May 2010.
  37. 37.
    Taverna, http://www.taverna.org.uk/. Accessed May 2010.
  38. 38.
    SWRC ontology, http://ontoware.org/projects/swrc/. Accessed Nov 2009.
  39. 39.
    Protégé, http://protege.stanford.edu/. Accessed May 2010.

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of SalfordSalfordUK

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