利用虚拟现实技术实现关联数据的可视化和探索。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Alexander J Kellmann, Max Postema, Joris de Keijser, Pjotr Svetachov, Rebecca C Wilson, Esther J van Enckevort, Morris A Swertz
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引用次数: 0

摘要

在本报告中,我们分析了使用虚拟现实(VR)作为导航和探索复杂知识图谱的方法。在过去的几十年里,链接数据技术(资源描述框架(RDF)和网络本体语言(OWL))已被证明对编码这类图具有重要价值,并且出现了许多对 RDF 进行交互式可视化的工具。然而,随着知识图谱变得越来越大,这些工具大多受到二维屏幕或三维投影的限制。因此,在本文中,我们评估了使用VR来可视化地探索SPARQL协议和RDF查询语言(SPARQL)(构造)查询的情况,包括一系列展示VR威力的教程视频(参见Graph2VR教程播放列表:https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH)。我们首先回顾了现有的关联数据可视化方法,然后报告了Graph2VR原型的创建情况。最后,我们报告了对使用 VR 探索关联数据图表的首次评估。我们的研究结果表明,大多数参与者都喜欢测试Graph2VR,并认为它是探索图表和发现数据的有用工具。可用性研究还为未来在VR中改进关联数据可视化提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualization and exploration of linked data using virtual reality.

In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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