Demo Data from EDBT 2026 / reviewers in the wild / expert
Dae-Ho Baek
dblp:91/4083
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2000
—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-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 |
Information retrieval · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › query formulation
boolean query formulation |
0.0 | 1 | 2000 | Latent semantic indexing model for boolean query formulation · SIGIR 2000 |
Information retrieval › retrieval models › latent semantic models
latent semantic indexing |
0.0 | 1 | 2000 | Latent semantic indexing model for boolean query formulation · SIGIR 2000 |
Information retrieval
retrieval models |
0.0 | 1 | 2000 | Latent semantic indexing model for boolean query formulation · SIGIR 2000 |
Methods — techniques the papers use, named apart from their topics
singular value decomposition · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2000 | Latent semantic indexing model for boolean query formulationabstractA new model named Boolean Latent Semantic Indexing model based on the Singular Value Decomposition and Boolean query formulation is introduced. While the Singular Value Decomposition alleviates the problems of lexical matching in the traditional information retrieval model, Boolean query formulation can help users to make precise representation of their information search needs. Retrieval experiments on a number of test collections seem to show that the proposed model achieves substantial performance gains over the Latent Semantic Indexing model. Dae-Ho Baek, Heui-Seok Lim, Hae-Chang Rim |
SIGIR | 1 |