@inproceedings{e16118ccb17e4c5f884029f63c9ee72d,
title = "Cartograph: Unlocking thematic cartography through semantic enhancement",
abstract = "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.",
keywords = "Maps, Neural networks, Semantic relatedness, Thematic cartography, Wikidata, Wikipedia",
author = "Shilad Sen and Swoap, {Anja Beth} and Qisheng Li and Brooke Boatman and Ilse Dippenaar and Rebecca Gold and Monica Ngo and Sarah Pujol and Bret Jackson and Brent Hecht",
note = "Publisher Copyright: {\textcopyright} 2017 ACM. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 22nd International Conference on Intelligent User Interfaces, IUI 2017 ; Conference date: 13-03-2017 Through 16-03-2017",
year = "2017",
month = mar,
day = "7",
doi = "10.1145/3025171.3025233",
language = "English (US)",
series = "International Conference on Intelligent User Interfaces, Proceedings IUI",
publisher = "Association for Computing Machinery",
pages = "179--190",
booktitle = "IUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces",
}