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Grid Services Complemented by Domain Ontology Supporting Biomedical Community

  • Maja Hadzic
  • Elizabeth Chang
Conference paper
  • 224 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3458)

Abstract

This paper describes the increasing role of ontologies in the context of Grid computing for obtaining, comparing and analyzing distributed heterogeneous scientific data. In the communities of people committed to a common goal, the management of resources and services becomes very important. We chose the application domain of human disease research and control. A characteristic of the domain is that trusted databases exist but their schemas are often poorly or not documented. The network of biomedical databases forms a loose federation of autonomous, distributed, heterogeneous data repositories ripe for information integration. Grid services will provide a dynamic way to use resources in such a large distributed scientific environment while the use of ontology enables the system to carryout reasoning at 3 levels: a) available information in all Bio-Databases (Grid nodes) worldwide, b) reasoning about the retrievable information from each node, c) reasoning about the retrieved information and presenting it in a meaningful format for users. We adopted the ontology design methodology of DOGMA and developed Generic Human Disease Ontology (GenDO) that contains common general information regarding human diseases. The information is represented in 4 “dimensions”: (a) disease types, (b) causes (c) symptoms and (d) treatments. We illustrate how this GenDO helps to produce Specific Human Disease Ontologies (SpeDO) on request. We show how the combination of two different but complementary techniques, namely Grid computing and ontology, results in a dynamic and intelligent information system. The two approaches together, being complementary, enable the system as a whole.

Keywords

Bipolar Disorder Grid Computing Grid Service Unify Medical Language System Scientific Environment 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Maja Hadzic
    • 1
  • Elizabeth Chang
    • 1
  1. 1.School of Information SystemsCurtin University of TechnologyPerthAustralia

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