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