TY - GEN

T1 - Towards Customizable Chart Visualizations of Tabular Data Using Knowledge Graphs

AU - Wiens, Vitalis

AU - Stocker, Markus

AU - Auer, Sören

N1 - Funding Information: Acknowledgments. This work is co-funded by the European Research Council project ScienceGRAPH (Grant agreement #819536). Additionally, we would like to thank our colleagues Mohamad Yaser Jaradeh and Kheir Eddine for valuable discussions and suggestions.

PY - 2020

Y1 - 2020

N2 - Scientific articles are typically published as PDF documents, thus rendering the extraction and analysis of results a cumbersome, error-prone, and often manual effort. New initiatives, such as ORKG, focus on transforming the content and results of scientific articles into structured, machine-readable representations using Semantic Web technologies. In this article, we focus on tabular data of scientific articles, which provide an organized and compressed representation of information. However, chart visualizations can additionally facilitate their comprehension. We present an approach that employs a human-in-the-loop paradigm during the data acquisition phase to define additional semantics for tabular data. The additional semantics guide the creation of chart visualizations for meaningful representations of tabular data. Our approach organizes tabular data into different information groups which are analyzed for the selection of suitable visualizations. The set of suitable visualizations serves as a user-driven selection of visual representations. Additionally, customization for visual representations provides the means for facilitating the understanding and sense-making of information.

AB - Scientific articles are typically published as PDF documents, thus rendering the extraction and analysis of results a cumbersome, error-prone, and often manual effort. New initiatives, such as ORKG, focus on transforming the content and results of scientific articles into structured, machine-readable representations using Semantic Web technologies. In this article, we focus on tabular data of scientific articles, which provide an organized and compressed representation of information. However, chart visualizations can additionally facilitate their comprehension. We present an approach that employs a human-in-the-loop paradigm during the data acquisition phase to define additional semantics for tabular data. The additional semantics guide the creation of chart visualizations for meaningful representations of tabular data. Our approach organizes tabular data into different information groups which are analyzed for the selection of suitable visualizations. The set of suitable visualizations serves as a user-driven selection of visual representations. Additionally, customization for visual representations provides the means for facilitating the understanding and sense-making of information.

KW - Customizable visualizations

KW - Information visualization

KW - Knowledge graphs

KW - Scholarly communication

UR - http://www.scopus.com/inward/record.url?scp=85097537238&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-64452-9_6

DO - 10.1007/978-3-030-64452-9_6

M3 - Conference contribution

AN - SCOPUS:85097537238

SN - 9783030644512

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 71

EP - 80

BT - Digital Libraries at Times of Massive Societal Transition

A2 - Ishita, Emi

A2 - Pang, Natalie Lee

A2 - Zhou, Lihong

T2 - 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020

Y2 - 30 November 2020 through 1 December 2020

ER -