Font attributes enrich knowledge maps and information retrieval

Brath, Richard; Banissi, Ebad

doi:10.1007/s00799-016-0168-4

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Optimizing typography is essential to perform three main tasks, namely: legibility (readability); localization and identification (highlight); and comparison (size). The first and most important task is the legibility (readability) of a written text, i.e., to understanding textual summaries [BB14b] or labels [BB16] in visualizations. Second, texts are rendered in visualizations to highlight certain aspects, therefore, localizing and identifying a specific text is an important task.…”

Section: Typical Analysis Tasks For Typographic Visualizationsmentioning

confidence: 99%

“…Similar to the design of Glyphs, the field of typography presents quality considerations mainly by stating design recommendations and guidelines. Brath and Banissi [BB14b] comprehensively review different font attributes and their perceptual and analytic effect on visualizations. In their paper, they list a set of font-specific properties (typography dimensions) that can be adjusted depending on the analysis task.…”

Section: Summary Of Approachesmentioning

confidence: 99%

“…Typography is used in different environments. For example, labeling a chart or a graph [BB16] has other constraints than displaying a complete text corpus in close‐ or distant‐reading views [BGN08,BB14b]. To optimize the use of typography in visualizations, different parameters [SOK*16, ACS*17] have to be altered; these include typeface, font size, font weight, color, orientation, intensity (boldness), spacing, case, border, background, underline, shadow.…”

Section: Text Datamentioning

confidence: 99%

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Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

109

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The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

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Section: Typical Analysis Tasks For Typographic Visualizationsmentioning

confidence: 99%

Section: Summary Of Approachesmentioning

confidence: 99%

Section: Text Datamentioning

confidence: 99%

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Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

109

View full text Add to dashboard Cite

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“…We use typographic visualizations [9] to distinguish between different content features. The visual representation is inspired by the work of Brath and Banissi [17] that shows the effectiveness of applying font-specific attributes to encode qualitative data. We use different font styles (e.g., bold font, normal font, italic) for different categories.…”

Section: Rule Visualizationmentioning

confidence: 99%

QuestionComb: A Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive Labeling

Sevastjanova

Jentner

Sperrle

et al. 2021

ACM Trans. Interact. Intell. Syst.

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Linguistic insight in the form of high-level relationships and rules in text builds the basis of our understanding of language. However, the data-driven generation of such structures often lacks labeled resources that can be used as training data for supervised machine learning. The creation of such ground-truth data is a time-consuming process that often requires domain expertise to resolve text ambiguities and characterize linguistic phenomena. Furthermore, the creation and refinement of machine learning models is often challenging for linguists as the models are often complex, in-transparent, and difficult to understand. To tackle these challenges, we present a visual analytics technique for interactive data labeling that applies concepts from gamification and explainable Artificial Intelligence (XAI) to support complex classification tasks. The visual-interactive labeling interface promotes the creation of effective training data. Visual explanations of learned rules unveil the decisions of the machine learning model and support iterative and interactive optimization. The gamification-inspired design guides the user through the labeling process and provides feedback on the model performance. As an instance of the proposed technique, we present QuestionComb , a workspace tailored to the task of question classification (i.e., in information-seeking vs. non-information-seeking questions). Our evaluation studies confirm that gamification concepts are beneficial to engage users through continuous feedback, offering an effective visual analytics technique when combined with active learning and XAI.

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