LinkDaViz – Automatic Binding of Linked Data to Visualizations

  • Klaudia Thellmann
  • Michael Galkin
  • Fabrizio Orlandi
  • Sören Auer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9366)


As the Web of Data is growing steadily, the demand for user-friendly means for exploring, analyzing and visualizing Linked Data is also increasing. The key challenge for visualizing Linked Data consists in providing a clear overview of the data and supporting non-technical users in finding suitable visualizations while hiding technical details of Linked Data and visualization configuration. In order to accomplish this, we propose a largely automatic workflow which guides users through the process of creating visualizations by automatically categorizing and binding data to visualization parameters. The approach is based on a heuristic analysis of the structure of the input data and a comprehensive visualization model facilitating the automatic binding between data and visualization parameters. The resulting assignments are ranked and presented to the user. With LinkDaViz we provide a web-based implementation of the approach and demonstrate the feasibility by an extended user and performance evaluation.


Data Property Recommendation Algorithm Structural Option Layout Option Input Data Model 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Klaudia Thellmann
    • 1
  • Michael Galkin
    • 1
  • Fabrizio Orlandi
    • 1
  • Sören Auer
    • 1
  1. 1.University of Bonn & Fraunhofer IAISBonnGermany

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