• DocumentCode
    2333546
  • Title

    Learning ontology from relational database

  • Author

    Li, Man ; Du, Xiao-yong ; Wang, Shan

  • Author_Institution
    Sch. of Inf., Renmin Univ. of China, Beijing, China
  • Volume
    6
  • fYear
    2005
  • fDate
    18-21 Aug. 2005
  • Firstpage
    3410
  • Abstract
    Ontology provides a shared and reusable piece of knowledge about a specific domain, and has been applied in many fields, such as semantic Web, e-commerce and information retrieval, etc. However, building ontology by hand is a very hard and error-prone task. Learning ontology from existing resources is a good solution. Because relational database is widely used for storing data and OWL is the latest standard recommended by W3C, this paper proposes an approach of learning OWL ontology from data in relational database. Compared with existing methods, the approach can acquire ontology from relational database automatically by using a group of learning rules instead of using a middle model. In addition, it can obtain OWL ontology, including the classes, properties, properties characteristics, cardinality and instances, while none of existing methods can acquire all of them. The proposed learning rules have been proven to be correct by practice.
  • Keywords
    Internet; learning (artificial intelligence); ontologies (artificial intelligence); relational databases; OWL; ontology Web language; ontology learning; relational database; Buildings; Information retrieval; Machine learning; OWL; Object oriented databases; Object oriented modeling; Ontologies; Relational databases; Resource description framework; Semantic Web; OWL; Ontology; Ontology Learning; Relational Database; Relational Model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
  • Conference_Location
    Guangzhou, China
  • Print_ISBN
    0-7803-9091-1
  • Type

    conf

  • DOI
    10.1109/ICMLC.2005.1527531
  • Filename
    1527531