DocumentCode :
3327946
Title :
Pruning Bio-Ontologies
Author :
Kim, Jong Woo ; Caralt, Jordi Conesa ; Hilliard, Julia K.
Author_Institution :
Dept. of Comput. Inf. Syst., Georgia State Univ., Atlanta, GA
fYear :
2007
fDate :
Jan. 2007
Abstract :
The explosive growth of biomedical data makes researchers and professionals recognize the need of developing large ontologies. However as the size of data increases and knowledge evolves, ontologies tend to grow big and large, and hence, retrieving manageable amount of information from large ontologies becomes a difficult and costly task. The purpose of this paper is to find and apply pruning methods from ontology engineering field to bio-ontologies as well as evaluate the results. These methods support systematic identification of relevant concepts and deletion of irrelevant part of an ontology. The paper shows how different pruning methods can be applied in bio-ontologies. To show the usefulness of pruning methods, a large bio-ontology called gene ontology is pruned to obtain a sub-ontology that contains only the relevant information a user is interested in
Keywords :
biology computing; data mining; genetics; information retrieval; ontologies (artificial intelligence); bio-ontologies pruning; biomedical data; gene ontology; information retrieval; ontology engineering; Bioinformatics; Biology computing; Information management; Information systems; Knowledge management; Maintenance; Ontologies; Systems biology; Terminology; Unified modeling language;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference on
Conference_Location :
Waikoloa, HI
ISSN :
1530-1605
Electronic_ISBN :
1530-1605
Type :
conf
DOI :
10.1109/HICSS.2007.455
Filename :
4076776
Link To Document :
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