VLDB 2026 Research / reviewers in the wild / expert
HaeMin Jung
dblp:184/5496
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0002-1182-9303ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 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.
| Artificial intelligence
1 paper |
Language models and text generation · 87% Graph learning · 13% | |
| Databases, data mining, and information retrieval
1 paper |
Knowledge graphs · 50% Recommender systems · 50% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › prompting › prompt engineering › prompt optimization
discrete prompt optimization |
1.0 | 1 | 2026 | Graph Discrete Prompt Optimization for Knowledge Graph Question Answering · WWW 2026 |
Natural language and speech › Language models and text generation › prompting
prompt engineering |
1.0 | 1 | 2026 | Graph Discrete Prompt Optimization for Knowledge Graph Question Answering · WWW 2026 |
Knowledge graphs › knowledge graph querying
knowledge graph question answering |
1.0 | 1 | 2026 | Graph Discrete Prompt Optimization for Knowledge Graph Question Answering · WWW 2026 |
Recommender systems
prompt tuning |
1.0 | 1 | 2026 | Graph Discrete Prompt Optimization for Knowledge Graph Question Answering · WWW 2026 |
Machine learning › Graph learning
graph representation learning |
0.3 | 1 | 2026 | Graph Discrete Prompt Optimization for Knowledge Graph Question Answering · WWW 2026 |
Methods — techniques the papers use, named apart from their topics
graph neural network · 2.0discrete prompt optimization · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Graph Discrete Prompt Optimization for Knowledge Graph Question Answering
Wooyoung Kim 0001, HaeMin Jung, Byeongjin Kim, Suhyeon Kwon, Wooju Kim |
WWW | 2 |
| 2020 | Automated conversion from natural language query to SPARQL query
HaeMin Jung, Wooju Kim |
J. Intell. Inf. Syst. | 1 |
| 2016 | Ontology-based model of law retrieval system for R&D projectsabstractResearch and development projects have close relationship with laws. In some cases, new technologies resulted from R&D projects can't be used because some statutes restrict them. The reason of this problem is that researchers don't know exactly which laws can affect their R&D projects. To solve the issue, we suggest a model for law retrieval system that can be used by researchers of R&D projects to find related statutes. Input of this model is a query document that describes the main contents of a project. By using ontology, legal terms are extracted from the document and statutes defining them are retrieved as a set of related laws. After this searching process, statutes are provided to researchers with their ranks, which are assigned using relevance scores we developed. By using this model, we can make a system for researchers to search a list of statutes that may affect R&D projects, and finally, they can adjust their project's direction by checking the list, preventing their works from being useless. Wooju Kim, Youna Lee, Donghe Kim, Minjae Won, HaeMin Jung |
ICEC | 5 |