Interlinking Linked Data Sources Using a Domain-Independent System

  • Khai Nguyen
  • Ryutaro Ichise
  • Bac Le
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7774)

Abstract

Linked data interlinking is the discovery of every owl:sameAs links between given data sources. An owl:sameAs link declares the homogeneous relation between two instances that co-refer to the same real-world object. Traditional methods compare two instances by predefined pairs of RDF predicates, and therefore they rely on the domain of the data. Recently, researchers have attempted to achieve the domain-independent goal by automatically building the linkage rules. However they still require the human curation for the labeled data as the input for learning process. In this paper, we present SLINT+, an interlinking system that is training-free and domain-independent. SLINT+ finds the important predicates of each data sources and combines them to form predicate alignments. The most useful alignments are then selected in the consideration of their confidence. Finally, SLINT+ uses selected predicate alignments as the guide for generating candidate and matching instances. Experimental results show that our system is very efficient when interlinking data sources in 119 different domains. The very considerable improvements on both precision and recall against recent systems are also reported.

Keywords

linked data interlinking domain-independent instance matching 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Khai Nguyen
    • 1
  • Ryutaro Ichise
    • 2
  • Bac Le
    • 1
  1. 1.University of ScienceHo Chi MinhVietnam
  2. 2.National Institute of InformaticsTokyoJapan

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