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Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking

  • Bernardo Pereira Nunes
  • Stefan Dietze
  • Marco Antonio Casanova
  • Ricardo Kawase
  • Besnik Fetahu
  • Wolfgang Nejdl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)

Abstract

One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.

Keywords

Semantic connectivity co-occurrence-based measure linked data data integration link detection semantic associations 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bernardo Pereira Nunes
    • 1
    • 2
  • Stefan Dietze
    • 2
  • Marco Antonio Casanova
    • 1
  • Ricardo Kawase
    • 2
  • Besnik Fetahu
    • 2
  • Wolfgang Nejdl
    • 2
  1. 1.Department of InformaticsPUC-RioRio de JaneiroBrazil
  2. 2.L3S Research CenterLeibniz University HannoverHannoverGermany

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