TY - JOUR
T1 - VizKG
T2 - 6th International Workshop on the Visualization and Interaction for Ontologies and Linked Data, VOILA! 2021
AU - Raissya, Hana
AU - Darari, Fariz
AU - Ekaputra, Fajar J.
N1 - Funding Information:
We thank the anonymous reviewers for their careful feedback. The dissemination of this work is funded by a grant from Program Kompetisi Kampus Merdeka (PK-KM) 2021 of Faculty of Computer Science, Universitas Indonesia. Furthermore, this work was sponsored by the Austrian Research Promotion Agency FFG under grant 877389 (OBARIS) and the Vienna Business Agency (VasQua project).
Publisher Copyright:
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2021
Y1 - 2021
N2 - Despite the rise of the knowledge graph (KG) popularity, understanding SPARQL query results from a KG can be challenging for users. The use of data visualization tools, e.g., Wikidata Query Service and YASGUI, can help address this challenge. However, existing tools are either focused just on a specific KG or only provided as a web interface. This paper proposes VizKG, a framework that provides a wide range of visualizations for SPARQL query results over KGs. VizKG aims to assist users in extracting patterns and insights from data in KGs, and hence supporting further KG analysis. VizKG features a wrapper that links SPARQL query results and external visualization libraries by mapping query result variables to the required visualization components, currently allowing for 24 types of visualizations. Not only that, VizKG also includes visualization recommendations for arbitrary SPARQL query results as well as extension mechanisms for additional visualization types. In our evaluation, the visualization recommendation feature of VizKG achieves an accuracy of 87.8%. To demonstrate the usefulness of VizKG in practical settings, this paper also reports on use case evaluation over various domains and KGs. A Python-based, Jupyter Notebook friendly implementation of VizKG is openly available at https://pypi.org/project/VizKG/.
AB - Despite the rise of the knowledge graph (KG) popularity, understanding SPARQL query results from a KG can be challenging for users. The use of data visualization tools, e.g., Wikidata Query Service and YASGUI, can help address this challenge. However, existing tools are either focused just on a specific KG or only provided as a web interface. This paper proposes VizKG, a framework that provides a wide range of visualizations for SPARQL query results over KGs. VizKG aims to assist users in extracting patterns and insights from data in KGs, and hence supporting further KG analysis. VizKG features a wrapper that links SPARQL query results and external visualization libraries by mapping query result variables to the required visualization components, currently allowing for 24 types of visualizations. Not only that, VizKG also includes visualization recommendations for arbitrary SPARQL query results as well as extension mechanisms for additional visualization types. In our evaluation, the visualization recommendation feature of VizKG achieves an accuracy of 87.8%. To demonstrate the usefulness of VizKG in practical settings, this paper also reports on use case evaluation over various domains and KGs. A Python-based, Jupyter Notebook friendly implementation of VizKG is openly available at https://pypi.org/project/VizKG/.
KW - Insights
KW - Knowledge graphs
KW - SPARQL
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85120791101&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85120791101
SN - 1613-0073
VL - 3023
SP - 95
EP - 102
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 25 October 2021
ER -