Using font attributes in knowledge maps and information retrieval
Richard Brath, Ebad K. Banissi · Research Open (London South Bank University) · 2014
Abstract. Font specific attributes, such as bold, italic and case can be used in knowledge mapping and information retrieval to encode addi-tional data in texts, lists and labels to increase data density of visualiza-tions; encode data quantitative data into search lists; and facilitate text skimming and refinement by visually promoting of words of interest. 1 Why Font Attributes? Information visualization (infovis) transforms data into visual representations. In knowledge mapping, visualizations are used to gain insight into the struc-ture of large scale information spaces. In knowledge maps, similar to geographic maps, text should have an inherent role to help viewer comprehend information, however, the use of font-specific attributes, such as bold, italic, caps, etc., in in-fovis is uncommon for encoding additional information. In information retrieval, search results may use a few font-attributes, e.g. bold, underline, serif/sans serif, to differentiate classes of metadata. The goal of this paper is to illustrate that font-specific attributes can be used to: 1) facilitate skimming texts such as abstracts or lead paragraphs; 2) encode quantitative data using a novel technique of proportional encoding in search results and facets; and 3) encode multiple data attributes in labels. 2