Mahmood Quaderi

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Mahmood Quaderi is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Explanatory semantic relatedness and explicit spatialization for exploratory search Cartography
Exploratory search
Geography
Giscience
Semantic relatedness
Spatialization
Text mining
Wikipedia
SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval English 2012 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. 0 0
Omnipedia: Bridging the Wikipedia Language Gap Wikipedia
Hyperlingual
Language barrier
User generated content
Text mining
International Conference on Human Factors in Computing Systems English 2012 We present Omnipedia, a system that allows Wikipedia readers to gain insight from up to 25 language editions ofWikipedia simultaneously. Omnipedia highlights the similarities and differences that exist among Wikipedia language editions, and makes salient information that is unique to each language as well as that which is shared more widely. We detail solutions to numerous front-end and algorithmic challenges inherent to providing users with a multilingual Wikipedia experience. These include visualizing content in a language-neutral way and aligning data in the face of diverse information organization strategies. We present a study of Omnipedia that characterizes how people interact with information using a multilingual lens. We found that users actively sought information exclusive to unfamiliar language editions and strategically compared how language editions defined concepts. Finally, we briefly discuss how Omnipedia generalizes to other domains facing language barriers. 0 0
Omnipedia: Bridging the Wikipedia language gap Hyperlingual
Language barrier
Text mining
User generated content
Wikipedia
Conference on Human Factors in Computing Systems - Proceedings English 2012 We present Omnipedia, a system that allows Wikipedia readers to gain insight from up to 25 language editions of Wikipedia simultaneously. Omnipedia highlights the similarities and differences that exist among Wikipedia language editions, and makes salient information that is unique to each language as well as that which is shared more widely. We detail solutions to numerous front-end and algorithmic challenges inherent to providing users with a multilingual Wikipedia experience. These include visualizing content in a language-neutral way and aligning data in the face of diverse information organization strategies. We present a study of Omnipedia that characterizes how people interact with information using a multilingual lens. We found that users actively sought information exclusive to unfamiliar language editions and strategically compared how language editions defined concepts. Finally, we briefly discuss how Omnipedia generalizes to other domains facing language barriers. Copyright 2012 ACM. 0 0