Using Semantic Web Technologies to Build a Community-Driven Knowledge Curation Platform for the Skeletal Dysplasia Domain

  • Tudor Groza
  • Andreas Zankl
  • Yuan-Fang Li
  • Jane Hunter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7032)

Abstract

In this paper we report on our on-going efforts in building SKELETOME – a community-driven knowledge curation platform for the skeletal dysplasia domain. SKELETOME introduces an ontology-driven knowledge engineering cycle that supports the continuous evolution of the domain knowledge. Newly submitted, undiagnosed patient cases undergo a collaborative diagnosis process that transforms them into well-structured case studies, classified, linked and discoverable based on their likely diagnosis(es). The paper presents the community requirements driving the design of the platform, the underlying implementation details and the results of a preliminary usability study. Because SKELETOME is built on Drupal 7, we discuss the limitations of some of its embedded Semantic Web components and describe a set of new modules, developed to handle these limitations (which will soon be released as open source to the community).

Keywords

Skeletal Dysplasia Bone Dysplasia Clinical Summary System Usability Scale Stickler Syndrome 
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.

References

  1. 1.
    Warman, M.L., et al.: Nosology and Classification of Genetic Skeletal Disorders: 2010 revision. American Journal of Medical Genetics Part A 155(5), 943–968 (2011)CrossRefGoogle Scholar
  2. 2.
    Hamosh, A., et al.: Online Mendelian Inheritance in Man (OMIM), a knowledge base of human genes and genetic disorders. Nucl. Acids Res. 33(1), 514–517 (2005)Google Scholar
  3. 3.
    Ashburner, M., et al.: Gene Ontology: Tool for the Unification of Biology. Nature Genetics 25(1), 25–29 (2000)CrossRefGoogle Scholar
  4. 4.
    Bairoch, A., et al.: The Universal Protein Resource (UniProt). Nucleic Acids Research 33(1), 154–159 (2005)Google Scholar
  5. 5.
    Hartel, F.W., et al.: Modeling a description logic vocabulary for cancer research. Journal of Biomedical Informatics 38(2), 114–129 (2005)CrossRefGoogle Scholar
  6. 6.
    Mabee, P.M., et al.: Phenotype ontologies: the bridge between genomics and evolution. Trends in Ecology and Evolution 22(7), 345–350 (2007)CrossRefGoogle Scholar
  7. 7.
    Hoffmann, R.: A wiki for the life sciences where authorship matters. Nature Genetics 40, 1047–1051 (2008)CrossRefGoogle Scholar
  8. 8.
    Gkoutos, G.V., et al.: Entity/Quality-Based Logical Definitions for the Human Skeletal Phenome using PATO. In: Proc. of the 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, pp. 7069–7072 (2009)Google Scholar
  9. 9.
    Jonquet, C., et al.: The open biomedical annotator. In: Proc. of the 2010 AMIA Summit of Translational Bioinformatics, San Francisco, California, US, pp. 56–60 (2010)Google Scholar
  10. 10.
    Dijkstra, E.W.: Selected Writings on Computing: A Personal Perspective. Springer, Heidelberg (1982)CrossRefMATHGoogle Scholar
  11. 11.
    Tudorache, T., et al.: Will Semantic Web Technologies Work for the Development of ICD-11? In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 257–272. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Brooke, J.: SUS: a “quick and dirty” usability scale. In: Jordan, P.W., Thomas, B., Weerdmeester, B.A., McClelland, A.L. (eds.) Usability Evaluation in Industry, pp. 184–194. Taylor and Francis, London (1996)Google Scholar
  13. 13.
    Corlosquet, S., et al.: Produce and Consume Linked Data with Drupal! In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 763–778. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Kroetzsch, M., et al.: Semantic Wikipedia. Journal of Web Semantics 5(4), 251–261 (2007)CrossRefGoogle Scholar
  15. 15.
    He, S., et al.: Collaborative Authoring of Biomedical Terminologies Using A Semantic Wiki. In: Proc. of AMIA 2009 Symposium, San Francisco, California, US, pp. 234–238 (2009)Google Scholar
  16. 16.
    Schaffert, S.: IkeWiki: A Semantic Wiki for Collaborative Knowledge Management. In: Proc. of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, Manchester, UK (2006)Google Scholar
  17. 17.
    Hoehndorf, R., et al.: BOWiki: an ontology-based wiki for annotation of data and integration of knowledge in biology. BMC Bioinformatics 10(S-5) (2009)Google Scholar
  18. 18.
    Giles, J.: Key biology databases go wiki. Nature 445, 691 (2007)CrossRefGoogle Scholar
  19. 19.
    Boeckmann, B., et al.: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 31(1), 365–370 (2003)CrossRefGoogle Scholar
  20. 20.
    Tudorache, T., et al.: Supporting Collaborative Ontology Development in Protégé. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 17–32. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tudor Groza
    • 1
  • Andreas Zankl
    • 2
    • 3
  • Yuan-Fang Li
    • 4
  • Jane Hunter
    • 1
  1. 1.School of ITEEThe University of QueenslandAustralia
  2. 2.Bone Dysplasia Research Group, UQ Centre for Clinical Research (UQCCR)The University of QueenslandAustralia
  3. 3.Genetic Health QueenslandRoyal Brisbane and Women’s HospitalHerstonAustralia
  4. 4.Monash UniversityMelbourneAustralia

Personalised recommendations