Enrichment of OBO ontologies.
This paper describes a frame-based integration of the three GO subontologies, the Chemical Entities of Biological Interest ontology, and the Cell Type Ontology in which relationships are modeled in a way that better captures the semantics between biological concepts represented by the terms, rather than between the terms themselves, than previous frame-based efforts. We also describe a methodology for creating suggested enriching assertions by identifying patterns in GO terms, mapping these patterns to new, specific relationships, and matching term substrings to concepts. Using this methodology, a predicted assertion was made for 62% of GO terms that matched one of 31 patterns, and 97% of these predicted assertions were assessed to be valid, resulting in an initial set of over 4000 assertions. Furthermore, this methodology programmatically integrates assertions into an ontology such that each assertion is fully consistent with respect to higher (i.e., more general) relevant class and slot levels.
Bada
Michael
M
University of Colorado at Denver and Health Sciences Center, Department of Pharmacology, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, P.O. Box 6511, Aurora, CO 80045, USA. mike.bada@uchsc.edu
Hunter
Lawrence
L
D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D006801:Humans; D007254:Information Science; D016247:Information Storage and Retrieval; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D009323:Natural Language Processing; D011381:Programming Languages; D012984:Software; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled