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What Should I Link to? Identifying Relevant Sources and Classes for Data Linking

  • Andriy Nikolov
  • Mathieu d’Aquin
  • Enrico Motta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7185)

Abstract

With more data repositories constantly being published on the Web, choosing appropriate data sources to interlink with newly published datasets becomes a non-trivial problem. It is necessary to choose both the repositories to link to and the relevant subsets of these repositories, which contain potentially matching individuals. In order to do this, detailed information about the content and structure of semantic repositories is often required. However, retrieving and processing such information for a potentially large number of datasets is practically unfeasible. In this paper, we propose an approach which utilises an existing semantic web index in order to identify potentially relevant datasets for interlinking and rank them. Furthermore, we adapt instance-based ontology schema matching to extract relevant subsets of selected data source and, in this way, pre-configure data linking tools.

Keywords

Jaccard Index Relevant Source Search Service Relevant Classis Ontology Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andriy Nikolov
    • 1
  • Mathieu d’Aquin
    • 1
  • Enrico Motta
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

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