Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels

Journal article

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. https://doi.org/10.1007/s00799-016-0168-4
AuthorsBrath, R and Banissi, E
Abstract

© 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.

Year2016
JournalInternational Journal on Digital Libraries
Journal citation18 (1), pp. 5-24
PublisherSpringer
ISSN1432-5012
Digital Object Identifier (DOI)https://doi.org/10.1007/s00799-016-0168-4
Publication dates
Print08 Feb 2016
Publication process dates
Deposited22 Aug 2016
Publisher's version
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File Access Level
Open
Additional information

© The Author(s) 2016. This article is published with open access at Springerlink.com

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https://openresearch.lsbu.ac.uk/item/87509

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