• 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