Visualization Systems for Linked Datasets
Maria Krommyda, Verena Kantere · 2020
The wide adoption of the RDF data model, as well as the Linked Open Data initiative, have made available large linked datasets that have the potential to offer invaluable knowledge. Accessing, evaluating and understanding these datasets as published, though, requires extensive training and experience in the field of the Semantic Web, making these valuable sources of information inaccessible to a wider audience. In the recent years, there have been many efforts to create systems that allow the visualization and exploration of this information. Some of there systems rely on techniques that allow them to limit the volume of the displayed information, by providing aggregated, filtered or summarized access to the datasets while others initialize the exploration of the dataset based on actions performed by the users, such as keyword searches and queries. The underlying technique is key for the sustainability of the system, the definition of the requirements that the input must comply with, the datasets that can be visualized as well as the visualization types provided. We present here a survey on these techniques, their strengths and weaknesses as well as the datasets that they can support. The survey will provide the reader with a deep understanding of the challenges regarding the visualization of large linked datasets, a categorization of the developed techniques to resolve them as well as an overview of the available systems and their functionalities.