Explanatory semantic relatedness and explicit spatialization for exploratory search
|Explanatory semantic relatedness and explicit spatialization for exploratory search|
|Author(s)||Hecht B., Carton S.H., Quaderi M., Schoning J., Raubal M., Gergle D., Downey D.|
|Published in||SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Keyword(s)||cartography, exploratory search, geography, giscience, semantic relatedness, spatialization, text mining, wikipedia (Extra: Exploratory search, geography, giscience, Semantic relatedness, Spatialization, Text mining, Wikipedia, Data mining, Hypertext systems, Information retrieval, Mapping, Maps, Search engines, Websites, Natural language processing systems)|
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Explanatory semantic relatedness and explicit spatialization for exploratory search is a 2012 conference paper written in English by Hecht B., Carton S.H., Quaderi M., Schoning J., Raubal M., Gergle D., Downey D. and published in SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.
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.
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