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    Ontology support for biomedical information resources

    116070_Ontology%20support%20for04561946.pdf (740.1Kb)
    Access Status
    Open access
    Authors
    Dillon, Tharam S.
    Chang, Elizabeth
    Hadzic, Maja
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Dillon, Tharam S. and Chang, Elizabeth and Hadzic, Maja. 2008. Ontology support for biomedical information resources, in Puuronen, S. and Pechenizkiy, M. Tsymbal, A. and Lee, Dah-Jye. (ed), 21st International Symposium on Computer Based Medical Systems, Jun 17 2008, pp.7-16, Jyvaskyla, Finland: Institute of Electrical and Electronics Engineers (IEEE) Computer Society
    Source Title
    Proceedings of 21st IEEE international symposium on computer based medical systems (CBMS 2008)
    Source Conference
    21st IEEE International Symposium on Computer Based Medical Systems (CBMS 2008)
    DOI
    10.1109/CBMS.2008.21
    ISBN
    9780769531656
    Faculty
    Curtin Business School
    School of Economics and Finance
    School
    Centre for Extended Enterprises and Business Intelligence
    Remarks

    Copyright © 2008 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/39639
    Collection
    • Curtin Research Publications
    Abstract

    The increasing body of distributed and heterogeneous information and the autonomous, heterogeneous and dynamic nature of information resources are important issues hindering effective and efficient data access, retrieval and knowledge sharing. The importance of ontologies has been recognised within the biomedical domain and work has begun on developing and sharing biomedical ontologies. In this paper, we define ontology and ontology commitments and explain the main characteristics and representations of ontology models. Ontologies are highly expressive knowledge models and as such increase expressiveness and intelligence of a system. We highlight the significance of ontologies in a variety of semi-automatic and automatic tasks, and provide an illustrative example of an ontology-based multi-agent system designed to intelligently retrieve information about human diseases from a number of heterogeneous and dispersed information resources.

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