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.
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb