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    Semantic annotation of morphological descriptions: an overall strategy

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    Author
    Cui, Hong
    Affiliation
    School of Information Resources and Library Science, University of Arizona, 1515 E. First Street, Tucson Arizona, 85719 USA
    Issue Date
    2010
    
    Metadata
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    Publisher
    BioMed Central
    Citation
    Cui BMC Bioinformatics 2010, 11:278 http://www.biomedcentral.com/1471-2105/11/278
    Journal
    BMC Bioinformatics
    Rights
    © 2010 Cui; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
    Collection Information
    This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
    Abstract
    BACKGROUND:Large volumes of morphological descriptions of whole organisms have been created as print or electronic text in a human-readable format. Converting the descriptions into computer- readable formats gives a new life to the valuable knowledge on biodiversity. Research in this area started 20 years ago, yet not sufficient progress has been made to produce an automated system that requires only minimal human intervention but works on descriptions of various plant and animal groups. This paper attempts to examine the hindering factors by identifying the mismatches between existing research and the characteristics of morphological descriptions.RESULTS:This paper reviews the techniques that have been used for automated annotation, reports exploratory results on characteristics of morphological descriptions as a genre, and identifies challenges facing automated annotation systems. Based on these criteria, the paper proposes an overall strategy for converting descriptions of various taxon groups with the least human effort.CONCLUSIONS:A combined unsupervised and supervised machine learning strategy is needed to construct domain ontologies and lexicons and to ultimately achieve automated semantic annotation of morphological descriptions. Further, we suggest that each effort in creating a new description or annotating an individual description collection should be shared and contribute to the "biodiversity information commons" for the Semantic Web. This cannot be done without a sound strategy and a close partnership between and among information scientists and biologists.
    EISSN
    1471-2105
    DOI
    10.1186/1471-2105-11-278
    Version
    Final published version
    Additional Links
    http://www.biomedcentral.com/1471-2105/11/278
    ae974a485f413a2113503eed53cd6c53
    10.1186/1471-2105-11-278
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    UA Faculty Publications

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