• DocumentCode
    2191745
  • Title

    Bridging Folksonomies and Domain Ontologies: Getting Out Non-taxonomic Relations

  • Author

    Trabelsi, Chiraz ; Ben Jrad, Aicha ; Ben Yahia, Sadok

  • Author_Institution
    Dept. of Comput. Sci., Fac. of Sci. of Tunis, Tunis, Tunisia
  • fYear
    2010
  • fDate
    13-13 Dec. 2010
  • Firstpage
    369
  • Lastpage
    379
  • Abstract
    Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to mention that ontologies can be of benefit for enhancing information retrieval metrics. In this paper, we introduce a novel approach for ontology learning from a folksonomy, which provide shared vocabularies and semantic relations between tags. The main thrust of the introduced approach stands in putting the focus on the discovery of nontaxonomic relationships. The latter are often neglected, even though they are of paramount importance from a semantic point of view. The discovery process heavily relies on triadic concepts to discover and select related tags and to extract and label non-taxonomically relationships between related tags and external sources for tags filtering and non-taxonomic relationships extraction. In addition, we also discuss a new approach to evaluate obtained relations in an automatic way against WordNet repository and presents promising results for a real world folksonomy.
  • Keywords
    data mining; identification technology; information retrieval; learning (artificial intelligence); ontologies (artificial intelligence); semantic Web; vocabulary; WordNet repository; data mining; discovery process; domain ontology; information retrieval metrics; lightweight conceptual structure; nontaxonomic relation; ontology learning; semantic relation; shared vocabulary; social bookmarking tool; tag filtering; triadic concept; Association rule; Conceptual Knowledge Discovery; Data Mining; Folksonomy; Ontology; Triadic Concept;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    978-1-4244-9244-2
  • Electronic_ISBN
    978-0-7695-4257-7
  • Type

    conf

  • DOI
    10.1109/ICDMW.2010.72
  • Filename
    5693322