Demo Data from EDBT 2026 / reviewers in the wild / expert
Kangpyo Lee
dblp:81/1479 · also Kang-Pyo Lee
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 |
Web and social media mining · 50% Information retrieval · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Web and social media mining
folksonomy |
0.1 | 1 | 2008 | Folksoviz: a subsumption-based folksonomy visualization using wikipedia texts · WWW 2008 |
Methods — techniques the papers use, named apart from their topics
statistical model · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Folksoviz: a subsumption-based folksonomy visualization using wikipedia textsabstractIn this paper, targeting del.icio.us tag data, we propose a method, FolksoViz, for deriving subsumption relationships between tags by using Wikipedia texts, and visualizing a folksonomy. To fulfill this method, we propose a statistical model for deriving subsumption relationships based on the frequency of each tag on the Wikipedia texts, as well as the TSD (Tag Sense Disambiguation) method for mapping each tag to a corresponding Wikipedia text. The derived subsumption pairs are visualized effectively on the screen. The experiment shows that the FolksoViz manages to find the correct subsumption pairs with high accuracy. Kangpyo Lee, Chungsu Jang, Hyoung-Joo Kim 0001 |
WWW | 1 |