Using Font Attributes in Knowledge Maps and Information Retrieval

Font specific attributes, such as bold, italic and case can be used in knowledge mapping and information retrieval to encode additional data in texts, lists and labels to increase data density of visualizations; 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 structure 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 infovis 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.