Enhancing acronym/abbreviation knowledge bases with semantic information

AMIA Annu Symp Proc. 2007 Oct 11;2007:731-5.


Objective: In the biomedical domain, a terminology knowledge base that associates acronyms/abbreviations (denoted as SFs) with the definitions (denoted as LFs) is highly needed. For the construction such terminology knowledge base, we investigate the feasibility to build a system automatically assigning semantic categories to LFs extracted from text.

Methods: Given a collection of pairs (SF,LF) derived from text, we i) assess the coverage of LFs and pairs (SF,LF) in the UMLS and justify the need of a semantic category assignment system; and ii) automatically derive name phrases annotated with semantic category and construct a system using machine learning.

Results: Utilizing ADAM, an existing collection of (SF,LF) pairs extracted from MEDLINE, our system achieved an f-measure of 87% when assigning eight UMLS-based semantic groups to LFs. The system has been incorporated into a web interface which integrates SF knowledge from multiple SF knowledge bases. Web site: http://gauss.dbb.georgetown.edu/liblab/SFThesurus.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Abbreviations as Topic*
  • Artificial Intelligence*
  • Internet
  • Knowledge Bases
  • Semantics
  • Terminology as Topic
  • Unified Medical Language System*