• DocumentCode
    658352
  • Title

    Automatic Domain Identification for Linked Open Data

  • Author

    Lalithsena, Sarasi ; Hitzler, Pascal ; Sheth, Amit ; Jain, Paril

  • Author_Institution
    Kno.e.sis Center, Wright State Univ., Dayton, OH, USA
  • Volume
    1
  • fYear
    2013
  • fDate
    17-20 Nov. 2013
  • Firstpage
    205
  • Lastpage
    212
  • Abstract
    Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.
  • Keywords
    Internet; data structures; Freebase; LOD; automatic domain identification; interlinked structured datasets; knowledge sources; linked open data; Animals; Drugs; Educational institutions; Rocks; TV; Vegetation; Dataset search; Domain Identification; Linked Open Data Cloud;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on
  • Conference_Location
    Atlanta, GA
  • Print_ISBN
    978-1-4799-2902-3
  • Type

    conf

  • DOI
    10.1109/WI-IAT.2013.206
  • Filename
    6690016