Cartograph: Unlocking thematic cartography through semantic enhancement

Shilad Sen, Anja Beth Swoap, Qisheng Li, Brooke Boatman, Ilse Dippenaar, Rebecca Gold, Monica Ngo, Sarah Pujol, Bret Jackson, Brent Hecht

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationIUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Electronic)9781450343480
StatePublished - Mar 7 2017
Event22nd International Conference on Intelligent User Interfaces, IUI 2017 - Limassol, Cyprus
Duration: Mar 13 2017Mar 16 2017

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI


Other22nd International Conference on Intelligent User Interfaces, IUI 2017


  • Maps
  • Neural networks
  • Semantic relatedness
  • Thematic cartography
  • Wikidata
  • Wikipedia

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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