KGScope: Interactive Visual Exploration of Knowledge Graphs with Embedding-based Guidance

Chao Wen Hsuan Yuan, Tzu Wei Yu, Jia Yu Pan, Wen Chieh Lin

Research output: Contribution to journalArticlepeer-review

Abstract

Knowledge graphs have been commonly used to represent relationships between entities and utilized in the industry to enhance service qualities. As knowledge graphs integrate data from a variety of sources, they can also be useful references for data analysts. However, there is a lack of effective tools to make the most of the rich information in knowledge graphs. Existing knowledge graph exploration systems are ineffective because they didn?t consider various users? needs and the characteristics of knowledge graphs. Exploratory approaches specifically designed for uncovering and summarizing insights in knowledge graphs have not been well studied yet. In this paper, we propose KGScope that supports interactive visual explorations and provides embedding-based guidance to derive insights from knowledge graphs. We demonstrate KGScope with usage scenarios and assess its efficacy in supporting knowledge graph exploration with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and aiding comprehensive exploration.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
DOIs
StateAccepted/In press - 2024

Keywords

  • Interactive visual exploration
  • Knowledge graph
  • Knowledge graph embedding

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