Semantic annotation of morphological descriptions: an overall strategy

Persistent Link:
http://hdl.handle.net/10150/610209
Title:
Semantic annotation of morphological descriptions: an overall strategy
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
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

Full metadata record

DC FieldValue Language
dc.contributor.authorCui, Hongen
dc.date.accessioned2016-05-20T09:01:08Z-
dc.date.available2016-05-20T09:01:08Z-
dc.date.issued2010en
dc.identifier.citationCui BMC Bioinformatics 2010, 11:278 http://www.biomedcentral.com/1471-2105/11/278en
dc.identifier.doi10.1186/1471-2105-11-278en
dc.identifier.urihttp://hdl.handle.net/10150/610209-
dc.description.abstractBACKGROUND: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.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1471-2105/11/278en
dc.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)en
dc.titleSemantic annotation of morphological descriptions: an overall strategyen
dc.typeArticleen
dc.identifier.eissn1471-2105en
dc.contributor.departmentSchool of Information Resources and Library Science, University of Arizona, 1515 E. First Street, Tucson Arizona, 85719 USAen
dc.identifier.journalBMC Bioinformaticsen
dc.description.collectioninformationThis 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.en
dc.eprint.versionFinal published versionen
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