@article{pena_linked_2014,
	title = {Linked {Open} {Data} {Visualization} {Revisited}: {A} {Survey}},
	url = {http://www.semantic-web-journal.net/system/files/swj937.pdf},
	abstract = {Mass adoption of the Semantic Web’s vision will not become a reality unless the benefits provided by data published under the Linked Open Data principles are understood by the majority of users. As technical and implementation details are far from being interesting for lay users, the ability of machines and algorithms to understand what the data is about should provide smarter summarisations of the available data. Visualization of Linked Open Data proposes itself as a perfect strategy to ease the access to information by all users, in order to save time learning what the dataset is about and without requiring knowledge on semantics. This article collects previous studies from the Information Visualization and the Exploratory Data Analysis fields in order to apply the lessons learned to Linked Open Data visualization. Datatype analysis and visualization tasks proposed by Ben Shneiderman are also added in the research to cover different visualization features. Finally, an evaluation of the current approaches is performed based on the dimensions previously exposed. The article ends with some conclusions extracted from the research.},
	language = {en},
	urldate = {2021-07-13},
	author = {Peña, Oscar and Aguilera, Unai and López-de-Ipiña, Diego},
	year = {2014},
}

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