Demo Data from EDBT 2026 / reviewers in the wild / expert
Prasath Rengarajan
dblp:195/8201
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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 · 62% Data mining · 38% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge graph |
0.3 | 1 | 2017 | Wikitop: Using Wikipedia Category Network to Generate Topic Trees · AAAI 2017 |
Information retrieval › indexing
document indexing |
0.3 | 1 | 2017 | Wikitop: Using Wikipedia Category Network to Generate Topic Trees · AAAI 2017 |
Data mining › text mining
topic detection |
0.3 | 1 | 2017 | Wikitop: Using Wikipedia Category Network to Generate Topic Trees · AAAI 2017 |
Information retrieval
text summarization |
0.1 | 1 | 2017 | Wikitop: Using Wikipedia Category Network to Generate Topic Trees · AAAI 2017 |
Information retrieval › document processing › document analysis
topic hierarchy generation |
0.1 | 1 | 2017 | Wikitop: Using Wikipedia Category Network to Generate Topic Trees · AAAI 2017 |
Methods — techniques the papers use, named apart from their topics
hierarchical classification · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Wikitop: Using Wikipedia Category Network to Generate Topic TreesabstractAutomated topic identification is an essential component invarious information retrieval and knowledge representationtasks such as automated summary generation, categorization search and document indexing. In this paper, we present the Wikitop system to automatically generate topic trees from the input text by performing hierarchical classification using the Wikipedia Category Network (WCN). Our preliminary results over a collection of 125 articles are encouraging and show potential of a robust methodology for automated topic tree generation. Saravana Kumar, Prasath Rengarajan, Arockia Xavier Annie Rayan |
AAAI | 2 |