Linked Open Data Visualization Revisited: A Survey. Peña, O., Aguilera, U., & López-de-Ipiña, D. 2014. 🏷️ /unread![Linked Open Data Visualization Revisited: A Survey [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex 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. 【摘要翻译】除非大多数用户都能理解根据关联开放数据原则发布的数据所带来的好处，否则大规模采用语义网的愿景将不会成为现实。由于技术和实施细节远非普通用户所感兴趣的，机器和算法理解数据内容的能力应该能够提供更智能的可用数据摘要。关联开放数据的可视化是一种完美的策略，它可以方便所有用户获取信息，从而节省了解数据集内容的时间，而且不需要语义学方面的知识。本文收集了之前在信息可视化和探索性数据分析领域的研究，以便将学到的经验应用到关联开放数据的可视化中。研究中还加入了本-施奈德曼（Ben Shneiderman）提出的数据类型分析和可视化任务，以涵盖不同的可视化特征。最后，根据之前揭示的维度对当前的方法进行了评估。文章的最后是研究得出的一些结论。
@article{pena2014,
title = {Linked {Open} {Data} {Visualization} {Revisited}: {A} {Survey}},
shorttitle = {重新审视关联开放数据可视化：调查},
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.
【摘要翻译】除非大多数用户都能理解根据关联开放数据原则发布的数据所带来的好处，否则大规模采用语义网的愿景将不会成为现实。由于技术和实施细节远非普通用户所感兴趣的，机器和算法理解数据内容的能力应该能够提供更智能的可用数据摘要。关联开放数据的可视化是一种完美的策略，它可以方便所有用户获取信息，从而节省了解数据集内容的时间，而且不需要语义学方面的知识。本文收集了之前在信息可视化和探索性数据分析领域的研究，以便将学到的经验应用到关联开放数据的可视化中。研究中还加入了本-施奈德曼（Ben Shneiderman）提出的数据类型分析和可视化任务，以涵盖不同的可视化特征。最后，根据之前揭示的维度对当前的方法进行了评估。文章的最后是研究得出的一些结论。},
language = {en},
urldate = {2021-07-13},
author = {Peña, Oscar and Aguilera, Unai and López-de-Ipiña, Diego},
year = {2014},
note = {🏷️ /unread},
keywords = {/unread},
}
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