Beyond Established Knowledge Graphs-Recommending Web Datasets for Data Linking

  • Mohamed Ben Ellefi
  • Zohra Bellahsene
  • Stefan Dietze
  • Konstantin Todorov
Conference paper

DOI: 10.1007/978-3-319-38791-8_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9671)
Cite this paper as:
Ben Ellefi M., Bellahsene Z., Dietze S., Todorov K. (2016) Beyond Established Knowledge Graphs-Recommending Web Datasets for Data Linking. In: Bozzon A., Cudre-Maroux P., Pautasso C. (eds) Web Engineering. ICWE 2016. Lecture Notes in Computer Science, vol 9671. Springer, Cham

Abstract

With the explosive growth of the Web of Data in terms of size and complexity, identifying suitable datasets to be linked, has become a challenging problem for data publishers. To understand the nature of the content of specific datasets, we adopt the notion of dataset profiles, where datasets are characterized through a set of topic annotations. In this paper, we adopt a collaborative filtering-like recommendation approach, which exploits both existing dataset profiles, as well as traditional dataset connectivity measures, in order to link arbitrary, non-profiled datasets into a global dataset-topic-graph. Our experiments, applied to all available Linked Datasets in the Linked Open Data (LOD) cloud, show an average recall of up to \(81\,\%\), which translates to an average reduction of the size of the original candidate dataset search space to up to \(86\,\%\). An additional contribution of this work is the provision of benchmarks for dataset interlinking recommendation systems.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohamed Ben Ellefi
    • 1
  • Zohra Bellahsene
    • 1
  • Stefan Dietze
    • 2
  • Konstantin Todorov
    • 1
  1. 1.LIRMMUniversity of MontpellierMontpellierFrance
  2. 2.L3S Research CenterLeibniz University HannoverHannoverGermany

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