Toward universal spatialization through Wikipedia-based semantic enhancement

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This article introduces Cartograph, a visualization system that harnesses the vast 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. Although 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 datasets 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. We present findings from two user studies exploring design choices and use of the system.

Original languageEnglish (US)
Article number12
JournalACM Transactions on Interactive Intelligent Systems
Volume9
Issue number2-3
DOIs
StatePublished - Apr 2019

Keywords

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

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Artificial Intelligence

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