Demo Data from SIGIR 2026 / reviewers in the wild / expert
Devin Garg
dblp:310/1601
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 77% Information extraction and text analysis · 23% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition › knowledge extraction
ontology-based information extraction |
0.6 | 1 | 2022 | An Ontological Approach towards Automatic Creation of Infographics from Formal Text (Student Abstract) · AAAI 2022 |
Visualization and visual analytics › visualization design
automated visualization |
0.6 | 1 | 2022 | An Ontological Approach towards Automatic Creation of Infographics from Formal Text (Student Abstract) · AAAI 2022 |
Visualization and visual analytics › data storytelling
infographic generation |
0.6 | 1 | 2022 | An Ontological Approach towards Automatic Creation of Infographics from Formal Text (Student Abstract) · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
ontological approach · 1.1end-to-end framework · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | An Ontological Approach towards Automatic Creation of Infographics from Formal Text (Student Abstract)abstractInfographics deal with representing data or information visually in a perceptually compelling manner. Recently, infographics have gained widespread popularity, giving rise to automated infographics synthesis from texts. Our research follows an ontological approach to automatically extract the necessary indicators from an input sentence and synthesize an infographic corresponding to it. This work includes (1) the creation of a dataset, (2) an end-to-end domain-agnostic framework, and (3) demonstrating the application of the proposed framework. The results demonstrate our framework's ability to extract the necessary textual cues from real-world textual descriptions (from various domains) and synthesize meaningful infographics. Devin Garg, Tanuj Agarwal, Chiranjoy Chattopadhyay |
AAAI | 1 |