Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.
BACKGROUND
Gene Ontology (GO) terms represent the standard for annotation and representation of molecular functions, biological processes and cellular compartments, but a large gap exists between the way concepts are represented in the ontology and how they are expressed in natural language text. The construction of highly specific GO terms is formulaic, consisting of parts and pieces from more simple terms.


RESULTS
We present two different types of manually generated rules to help capture the variation of how GO terms can appear in natural language text. The first set of rules takes into account the compositional nature of GO and recursively decomposes the terms into their smallest constituent parts. The second set of rules generates derivational variations of these smaller terms and compositionally combines all generated variants to form the original term. By applying both types of rules, new synonyms are generated for two-thirds of all GO terms and an increase in F-measure performance for recognition of GO on the CRAFT corpus from 0.498 to 0.636 is observed. Additionally, we evaluated the combination of both types of rules over one million full text documents from Elsevier; manual validation and error analysis show we are able to recognize GO concepts with reasonable accuracy (88 %) based on random sampling of annotations.


CONCLUSIONS
In this work we present a set of simple synonym generation rules that utilize the highly compositional and formulaic nature of the Gene Ontology concepts. We illustrate how the generated synonyms aid in improving recognition of GO concepts on two different biomedical corpora. We discuss other applications of our rules for GO ontology quality assurance, explore the issue of overgeneration, and provide examples of how similar methodologies could be applied to other biomedical terminologies. Additionally, we provide all generated synonyms for use by the text-mining community.
Computational Bioscience, University of Colorado School of Medicine, Aurora, CO 80045, USA. christopher.funk@ucdenver.edu.
Christopher S
CS
Funk
Computational Bioscience, University of Colorado School of Medicine, Aurora, CO 80045, USA.
K Bretonnel
KB
Cohen
Computational Bioscience, University of Colorado School of Medicine, Aurora, CO 80045, USA.
Lawrence E
LE
Hunter
Department of Computing and Information Systems, University of Melbourne, Parkville, Melbourne, 3010, Australia.|Health and Biomedical Informatics Centre, University of Melbourne, Parkville, Melbourne, 3010, Australia.
Karin M
KM
Verspoor
D057225:Data Mining / Q000379:methods*; D063990:Gene Ontology; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D012660:Semantics
Biomedical concept recognition; Gene ontology; Named entity recognition; Text-mining