Advertisement – A Linked Information System for Rare Diseases

  • Romy Elze
  • Tom-Michael Hesse
  • Michael Martin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7058)


The challenge of developing information systems for rare diseases is in harmonizing social care conditions and health care conditions with the focus on personalization and patient autonomy. Knowledge about the most rare diseases is limited, which is the result of poorly funded research and the existence of only a few specialized experts. Furthermore, the treatment and care of the affected patients is very complex, cost-intensive, time critical, and involved stakeholders are very heterogeneous. The information needed by the patient depends on his or her personal situation and constitution. To support the information logistics between patients of rare diseases and (all) other stakeholders (e.g. physicians, therapists, and researchers), we developed an information system with Linked Open Data technologies in order to create a platform and tool independent solution addressing the heterogeneity of the stakeholders. To engineer system and data model requirements of our approach we analyzed the rare disease Amyotrophic Lateral Sclerosis (ALS), which have wide-spreaded characteristics. The resulting formal knowledge representation was encoded in OWL, which allows, for instance, a modular development of complex areas and also the re-usage of existing knowledge bases.


Amyotrophic Lateral Sclerosis Rare Disease Resource Description Framework Link Open Data Resource Description Framework Data 
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.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semantic Web Inf. Syst. 5(3), 1–22 (2009)CrossRefGoogle Scholar
  2. 2.
    BMBF. Rare diseases (2010),
  3. 3.
    Cañas, A.J., Carff, R., Hill, G., Carvalho, M., Arguedas, M., Eskridge, T.C., Lott, J., Carvajal, R.: Concept Maps: Integrating Knowledge and Information Visualization. In: Tergan, S.-O., Keller, T. (eds.) Knowledge and Information Visualization. LNCS, vol. 3426, pp. 205–219. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Eidt, D., et al.: Maßnahmen zur Verbesserung der gesundheitlichen Situation von Menschen mit Seltenen Erkrankungen in Deutschland, Berlin (2009)Google Scholar
  5. 5.
    Gastl, R., Ludolph, A.C.: Amyotrophe Lateralsklerose. Der Nervenarzt 78, 1449–1459 (2007)CrossRefGoogle Scholar
  6. 6.
    Gmez-Prez, A., Benjamins, R.: Overview of knowledge sharing and reuse components: Ontologies and problem-solving methods. In: 16th International Joint Conference on Artificial Intelligence (IJCAI 1999) Workshop KRR5: Ontologies and Problem-Solving Methods: Lesson Learned and Future Trends, Stockholm, Sweden, vol. 18 (1999); IJCAI and the Scandinavian AI Societies. CEUR Workshop ProceedingsGoogle Scholar
  7. 7.
    Heino, N., Dietzold, S., Martin, M., Auer, S.: Developing Semantic Web Applications with the Ontowiki Framework. In: Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S. (eds.) Networked Knowledge - Networked Media. SCI, vol. 221, pp. 61–77. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Kalra, D., Beale, T., Heard, S.: The openehr foundation. Stud Health Technol. Inform. 115, 153–173 (2005); Kalra, D., Beale, T., Heard, S.: Netherlands Studies in health technology and informatics. Stud. Health Technol. Inform. 115, 153–173 (2005)Google Scholar
  9. 9.
    Kempf, K.U.: Analyse des Phänotyps von an familiärer amyothropher Lateralsklerose erkrankter Familien ohne Mutation der Superoxid-1-dismutase: Eine Untersuchung zur Vorbereitung von Kopplungsanalysen, Ulm (2006)Google Scholar
  10. 10.
    Kuschel, F.: Die medizinische Versorgung erwachsener Patienten mit Muskelerkrankungen, Berlin (2006)Google Scholar
  11. 11.
    Manola, F., Miller, E.: Rdf primer (2004),
  12. 12.
    Mitchell, J.D., Borasio, G.D.: Amyotrophic lateral sclerosis. The Lancet 369(9578), 2031 (2007)CrossRefGoogle Scholar
  13. 13.
    Musca, G., Cuccurullo, O.: Chronic Heart Failure management program. BMC Geriatrics 10, 1 (2010)CrossRefGoogle Scholar
  14. 14.
    Möller, M., Ernst, P., Sintek, M., Biedert, R., Dengel, A., Sonntag, D.: Representing the international classification of diseases version 10 in owl. In: Proc. of KEOD, Spain (2010)Google Scholar
  15. 15.
    Rieß, C., Heino, N., Tramp, S., Auer, S.: EvoPat – Pattern-Based Evolution and Refactoring of RDF Knowledge Bases. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 647–662. Springer, Heidelberg (2010), CrossRefGoogle Scholar
  16. 16.
    Taruscio, D., Seyoum, I.M.: Tackling the problem of rare diseases in public health: the Italian approach. Community Genet. 6, 123–124 (2003)Google Scholar
  17. 17.
    Tramp, S., Heino, N., Auer, S., Frischmuth, P.: RDFauthor: Employing RDFa for Collaborative Knowledge Engineering. In: Cimiano, P., Pinto, H.S. (eds.) EKAW 2010. LNCS, vol. 6317, pp. 90–104. Springer, Heidelberg (2010), CrossRefGoogle Scholar
  18. 18.
    W3C. OWL Web Ontology Language Overview, W3C Recommendation. Online (February 2004),
  19. 19.
    WHO. Icf the international classification of functioning, disability and health (2002)Google Scholar
  20. 20.
    WHO. International classification of diseases (icd) (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Romy Elze
    • 1
  • Tom-Michael Hesse
    • 2
  • Michael Martin
    • 1
  1. 1.Institut for Computer Science, Department of Business Information SystemsUniversity of LeipzigGermany
  2. 2.Institute for Applied Informatics (InfAI) e.V.Germany

Personalised recommendations