Abstract
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.
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Thellmann, K., Galkin, M., Orlandi, F., Auer, S. (2015). LinkDaViz – Automatic Binding of Linked Data to Visualizations. In: Arenas, M., et al. The Semantic Web - ISWC 2015. ISWC 2015. Lecture Notes in Computer Science(), vol 9366. Springer, Cham. https://doi.org/10.1007/978-3-319-25007-6_9
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DOI: https://doi.org/10.1007/978-3-319-25007-6_9
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