Details

Autor(en) / Beteiligte

• Hsuan Yuan, Chao-Wen
• Yu, Tzu-Wei
• Pan, Jia-Yu
• Lin, Wen-Chieh
• Goyal, Tesh
• Kristensson, Per Ola
• Peters, Anicia
• Schmidt, Albrecht
• Väänänen, Kaisa

Titel

Interactive Visual Exploration of Knowledge Graphs with Embedding-based Guidance

Ist Teil von

• Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023, p.1-8

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2023

Quelle

ACM Digital Library

Beschreibungen/Notizen

• 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 human users. However, there is a lack of effective tools for data analysts 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 a usage scenario 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.