title: Word add-in for ontology recognition: semantic enrichment of scientific literature.
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.
authors.affiliation: Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA 92093-0444, USA. jlfink@ucsd.edu
authors.forename: J Lynn
authors.initials: JL
authors.lastname: Fink
authors.forename: Pablo
authors.initials: P
authors.lastname: Fernicola
authors.forename: Rahul
authors.initials: R
authors.lastname: Chandran
authors.forename: Savas
authors.initials: S
authors.lastname: Parastatidis
authors.forename: Alex
authors.initials: A
authors.lastname: Wade
authors.forename: Oscar
authors.initials: O
authors.lastname: Naim
authors.forename: Gregory B
authors.initials: GB
authors.lastname: Quinn
authors.forename: Philip E
authors.initials: PE
authors.lastname: Bourne
mesh_terms: D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D011381:Programming Languages; D011642:Publications; D012660:Semantics; D018875:Vocabulary, Controlled