VizKG: A framework for visualizing SPARQL query results over knowledge graphs

Hana Raissya, Fariz Darari, Fajar J. Ekaputra

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


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

Original languageEnglish
Pages (from-to)95-102
Number of pages8
JournalCEUR Workshop Proceedings
Publication statusPublished - 2021
Event6th International Workshop on the Visualization and Interaction for Ontologies and Linked Data, VOILA! 2021 - Virtual, Online
Duration: 25 Oct 2021 → …


  • Insights
  • Knowledge graphs
  • Visualization


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