TY - GEN
T1 - Explanatory semantic relatedness and explicit spatialization for exploratory search
AU - Hecht, Brent
AU - Carton, Samuel H.
AU - Quaderi, Mahmood
AU - Schöning, Johannes
AU - Raubal, Martin
AU - Gergle, Darren
AU - Downey, Doug
PY - 2012
Y1 - 2012
N2 - Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram - from a periodic table, to a parliamentary seating chart, to a world map - as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.
AB - Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram - from a periodic table, to a parliamentary seating chart, to a world map - as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.
KW - cartography
KW - exploratory search
KW - geography
KW - giscience
KW - semantic relatedness
KW - spatialization
KW - text mining
KW - wikipedia
UR - http://www.scopus.com/inward/record.url?scp=84866623262&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866623262&partnerID=8YFLogxK
U2 - 10.1145/2348283.2348341
DO - 10.1145/2348283.2348341
M3 - Conference contribution
AN - SCOPUS:84866623262
SN - 9781450316583
T3 - SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 415
EP - 424
BT - SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
T2 - 35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012
Y2 - 12 August 2012 through 16 August 2012
ER -