Enhancing acronym/abbreviation knowledge bases with semantic information.
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.
Torii
Manabu
M
Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC, USA.
Liu
Hongfang
H
D000004:Abbreviations as Topic; D001185:Artificial Intelligence; D020407:Internet; D051188:Knowledge Bases; D016239:MEDLINE; D012660:Semantics; D009626:Terminology as Topic; D017432:Unified Medical Language System