Word add-in for ontology recognition: semantic enrichment of scientific literature

Fink, J. Lynn, Fernicola, Pablo, Chandran, Rahul, Parastatidis, Savas, Wade, Alex, Naim, Oscar, Quinn, Gregory B. and Bourne, Philip E. (2010) Word add-in for ontology recognition: semantic enrichment of scientific literature. Bmc Bioinformatics, 11 24: Article number 103. doi:10.1186/1471-2105-11-103


Author Fink, J. Lynn
Fernicola, Pablo
Chandran, Rahul
Parastatidis, Savas
Wade, Alex
Naim, Oscar
Quinn, Gregory B.
Bourne, Philip E.
Title Word add-in for ontology recognition: semantic enrichment of scientific literature
Journal name Bmc Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2010-02
Sub-type Article (original research)
DOI 10.1186/1471-2105-11-103
Open Access Status DOI
Volume 11
Issue 24
Start page Article number 103
Total pages 8
Place of publication United Kingdom
Publisher BioMed Central Ltd.
Language eng
Formatted abstract
Background:
In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles.

Results:
The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit.

Conclusions:
The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.
Keyword National-Center
Biotechnology-Information
Biomedical Ontologies
Nih Roadmap
Text
Knowledge
Database
Biology
Virus
Gene
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collections: ERA 2012 Admin Only
Institute for Molecular Bioscience - Publications
 
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