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Chao-Wen Hsuan Yuan

dblp:345/1802 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0008-7134-9089ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Knowledge graphs · 67% Data mining · 33%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge graphs
knowledge graph embedding
0.812024
KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance · IEEE Trans. Vis. Comput. Graph. 2024
Knowledge graphs
knowledge graph exploration
0.812024
KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance · IEEE Trans. Vis. Comput. Graph. 2024
Data mining › exploratory data analysis
visual exploration
0.812024
KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics › visual analytics
interactive visual analysis
0.212024
KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

user study · 1.5knowledge graph embedding · 1.5
YearPublicationVenuePosition
2024 KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance
abstract
Knowledge graphs have been commonly used to represent relationships between entities and are 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 did not consider various user needs and characteristics of knowledge graphs. Exploratory approaches specifically designed to uncover and summarize insights in knowledge graphs have not been well studied yet. In this article, 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 the exploration of knowledge graphs with a user study. The results show that KGScope supports knowledge graph exploration effectively by providing useful information and helping explore the entire network.
Chao-Wen Hsuan Yuan, Tzu-Wei Yu, Jia-Yu Pan, Wen-Chieh Lin
IEEE Trans. Vis. Comput. Graph.1