制图:通过语义增强解锁空间可视化

Shilad Sen, Anja Beth Swoap, Qisheng Li, Brooke Boatman, I. Dippenaar, Rebecca Gold, Monica Ngo, Sarah Pujol, Bret Jackson, Brent J. Hecht
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引用次数: 29

摘要

这篇文章介绍了Cartograph,一个可视化系统,它利用维基百科中编码的大量世界知识来创建几乎任何数据的主题地图。Cartograph扩展了以前使用地理方法可视化非空间数据的系统。虽然这些系统需要具有现有语义结构的数据,但Cartograph通过使用从维基百科中提取的语义信息增强输入数据集,为更多种类的数据集解锁了空间可视化。Cartograph的地图嵌入使用经过维基百科文章内容和用户导航行为训练的神经网络。使用这些嵌入,系统可以揭示在原始数据集中不相关的点之间的联系,但在意义上是相关的,因此在地图上紧密地嵌入在一起。我们描述了系统的设计和我们遇到的主要挑战,并介绍了探索性用户研究的结果
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cartograph: Unlocking Spatial Visualization Through Semantic Enhancement
This paper introduces Cartograph, a visualization system that harnesses the vast amount of world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. While these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph's map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original data sets, but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered, and we present findings from an exploratory user study
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