Discovering Missing Links in Large-Scale Linked Data

  • Nam Hau
  • Ryutaro Ichise
  • Bac Le
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7803)

Abstract

The explosion of linked data is creating sparse connection networks, primarily because more and more missing links among difference data sources are resulting from asynchronous and independent database development. DHR was proposed in other research to discover these links.However, DHR has limitations in a distributed environment. For example, while deploying on a distributed SPARQL server, the data transfer usually causes overhead on the network. Therefore, we propose a new method of detecting a missing link based on DHR. The method consists of two stages: finding the frequent graph and matching the similarity. In this paper, we enhance some features in the two stages to reduce the data flow before querying. We conduct an experiment using geographic data sources with a large number of triples to discover the missing links and compare the accuracy of our proposed matching method with DHR and the primitive mix similarity method. The experimental results show that our method can reduce a large amount of data flow on a network and increase the accuracy of discovering missing links.

Keywords

graph mining linked data link prediction distributed RDF data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nam Hau
    • 1
  • Ryutaro Ichise
    • 2
  • Bac Le
    • 3
  1. 1.University of TechnologyHo Chi Minh CityVietnam
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.National Science UniversityHo Chi Minh CityVietnam

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