EDBT 2026 Demo / reviewers in the wild / expert
Tzu-Wei Yu
dblp:321/9343
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0003-5053-9688ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs
knowledge graph embedding |
0.8 | 1 | 2024 | KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance · IEEE Trans. Vis. Comput. Graph. 2024 |
Knowledge graphs
knowledge graph exploration |
0.8 | 1 | 2024 | KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance · IEEE Trans. Vis. Comput. Graph. 2024 |
Data mining › exploratory data analysis
visual exploration |
0.8 | 1 | 2024 | 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 |
0.2 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based GuidanceabstractKnowledge 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. | 2 |