• DocumentCode
    2052129
  • Title

    WikiOnto: A System for Semi-automatic Extraction and Modeling of Ontologies Using Wikipedia XML Corpus

  • Author

    De Silva, L.N. ; Jayaratne, Lakshman

  • Author_Institution
    Sch. of Comput., Univ. of Colombo, Colombo, Sri Lanka
  • fYear
    2009
  • fDate
    14-16 Sept. 2009
  • Firstpage
    571
  • Lastpage
    576
  • Abstract
    This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus of one of the largest knowledge bases in the world - the Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using Natural Language Processing (NLP) and other Machine Learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.
  • Keywords
    XML; learning (artificial intelligence); natural language processing; ontologies (artificial intelligence); text analysis; WikiOnto; Wikipedia XML corpus; knowledge bases; machine learning; natural language processing; semi-automatic ontology extraction; semi-automatic ontology modeling; semi-structured document corpora; Buildings; Data mining; Machine learning; Natural language processing; Ontologies; Prototypes; Relational databases; Semantic Web; Wikipedia; XML; Ontology; Ontology Extraction; Ontology Modeling; Wikipedia XML Corpus;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Semantic Computing, 2009. ICSC '09. IEEE International Conference on
  • Conference_Location
    Berkeley, CA
  • Print_ISBN
    978-1-4244-4962-0
  • Electronic_ISBN
    978-0-7695-3800-6
  • Type

    conf

  • DOI
    10.1109/ICSC.2009.93
  • Filename
    5298539