%0 Conference Paper %1 citeulike:14310774 %A Sen, Shilad %A Swoap, Anja B. %A Li, Qisheng %A Boatman, Brooke %A Dippenaar, Ilse %A Gold, Rebecca %A Ngo, Monica %A Pujol, Sarah %A Jackson, Bret %A Hecht, Brent %B Proceedings of the 22Nd International Conference on Intelligent User Interfaces %C New York, NY, USA %D 2017 %I ACM %K information-map information-visualization iui2017 similarity wikipedia %P 179--190 %R 10.1145/3025171.3025233 %T Cartograph: Unlocking Spatial Visualization Through Semantic Enhancement %U http://dx.doi.org/10.1145/3025171.3025233 %X 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 %@ 978-1-4503-4348-0

@inproceedings{citeulike:14310774, 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}}, added-at = {2017-11-15T17:02:25.000+0100}, address = {New York, NY, USA}, author = {Sen, Shilad and Swoap, Anja B. and Li, Qisheng and Boatman, Brooke and Dippenaar, Ilse and Gold, Rebecca and Ngo, Monica and Pujol, Sarah and Jackson, Bret and Hecht, Brent}, biburl = {https://www.bibsonomy.org/bibtex/2cf669edbc89fc6a8509ade3895e13990/brusilovsky}, booktitle = {Proceedings of the 22Nd International Conference on Intelligent User Interfaces}, citeulike-article-id = {14310774}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3025233}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/3025171.3025233}, comment = {(private-note)Semantic similarity from Wikipedia allows to build a spatial map of Wikipedia. It could be used to present and aggregate a range of interesting information just like geographic map visualization}, doi = {10.1145/3025171.3025233}, interhash = {c218d19741057b7d68fabfe894adeb75}, intrahash = {cf669edbc89fc6a8509ade3895e13990}, isbn = {978-1-4503-4348-0}, keywords = {information-map information-visualization iui2017 similarity wikipedia}, location = {Limassol, Cyprus}, pages = {179--190}, posted-at = {2017-03-14 12:45:21}, priority = {2}, publisher = {ACM}, series = {IUI '17}, timestamp = {2020-07-28T17:28:50.000+0200}, title = {{Cartograph: Unlocking Spatial Visualization Through Semantic Enhancement}}, url = {http://dx.doi.org/10.1145/3025171.3025233}, year = 2017 }