Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels
Brath, R and Banissi, E (2016). Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels. International Journal on Digital Libraries. 18 (1), pp. 5-24.
|Authors||Brath, R and Banissi, E|
© 2016 The Author(s)Typography is overlooked in knowledge maps (KM) and information retrieval (IR), and some deficiencies in these systems can potentially be improved by encoding information into font attributes. A review of font use across domains is used to itemize font attributes and information visualization theory is used to characterize each attribute. Tasks associated with KM and IR, such as skimming, opinion analysis, character analysis, topic modelling and sentiment analysis can be aided through the use of novel representations using font attributes such as skim formatting, proportional encoding, textual stem and leaf plots and multi-attribute labels.
|Journal||International Journal on Digital Libraries|
|Journal citation||18 (1), pp. 5-24|
|Digital Object Identifier (DOI)||doi:10.1007/s00799-016-0168-4|
|08 Feb 2016|
|Publication process dates|
|Deposited||22 Aug 2016|
CC BY 4.0
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© The Author(s) 2016. This article is published with open access at Springerlink.com
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