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

Richard Brath; Ebad Banissi

doi:10.1007/s00799-016-0168-4

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

Richard Brath
, Ebad Banissi

Computer Science

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Pages (from-to)	5-24
Journal	International Journal on Digital Libraries
DOIs	https://doi.org/10.1007/s00799-016-0168-4
Publication status	Published - 8 Feb 2016

Access to Document

10.1007/s00799-016-0168-4Licence: CC BY 4.0

Cite this

@article{cfde92e32a0943e39acbc32059a7f786,

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

abstract = "{\textcopyright} 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.",

author = "Richard Brath and Ebad Banissi",

year = "2016",

month = feb,

day = "8",

doi = "10.1007/s00799-016-0168-4",

language = "English",

pages = "5--24",

journal = "International Journal on Digital Libraries",

issn = "1432-5012",

publisher = "Springer Verlag",

}

TY - JOUR

T1 - Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels

AU - Brath, Richard

AU - Banissi, Ebad

PY - 2016/2/8

Y1 - 2016/2/8

N2 - © 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.

AB - © 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.

U2 - 10.1007/s00799-016-0168-4

DO - 10.1007/s00799-016-0168-4

M3 - Article

SN - 1432-5012

SP - 5

EP - 24

JO - International Journal on Digital Libraries

JF - International Journal on Digital Libraries

ER -

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

Abstract

Access to Document

Other files and links

Fingerprint

Cite this