TripleCheckMate: A Tool for Crowdsourcing the Quality Assessment of Linked Data

  • Dimitris Kontokostas
  • Amrapali Zaveri
  • Sören Auer
  • Jens Lehmann
Part of the Communications in Computer and Information Science book series (CCIS, volume 394)

Abstract

Linked Open Data (LOD) comprises of an unprecedented volume of structured datasets on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowdsourced and even extracted data of relatively low quality. We present a methodology for assessing the quality of linked data resources, which comprises of a manual and a semi-automatic process. In this paper we focus on the manual process where the first phase includes the detection of common quality problems and their representation in a quality problem taxonomy. The second phase comprises of the evaluation of a large number of individual resources, according to the quality problem taxonomy via crowdsourcing. This process is implemented by the tool TripleCheckMate wherein a user assesses an individual resource and evaluates each fact for correctness. This paper focuses on describing the methodology, quality taxonomy and the tools’ system architecture, user perspective and extensibility.

Keywords

Data Quality Linked Data DBpedia 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dimitris Kontokostas
    • 1
  • Amrapali Zaveri
    • 1
  • Sören Auer
    • 2
  • Jens Lehmann
    • 1
  1. 1.AKSW/BISUniversitt LeipzigGermany
  2. 2.CS/EISUniversität BonnGermany

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