Demo Data from EDBT 2026 / reviewers in the wild / expert
Nabin Khanal
dblp:387/5647
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0007-1972-7022ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 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.
| Databases, data mining, and information retrieval
1 paper |
Query processing and optimization · 33% Data models and query languages · 33% Information retrieval · 33% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Query processing and optimization
interactive data exploration |
0.8 | 1 | 2024 | FathomGPT: A natural language interface for interactively exploring ocean science data · UIST 2024 |
Data models and query languages
natural language interface |
0.8 | 1 | 2024 | FathomGPT: A natural language interface for interactively exploring ocean science data · UIST 2024 |
Methods — techniques the papers use, named apart from their topics
prompt modification · 1.5large language model · 1.5fine-tuning · 1.5ablation study · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | FathomGPT: A natural language interface for interactively exploring ocean science dataabstractWe introduce FathomGPT, an open source system for the interactive investigation of ocean science data via a natural language interface. FathomGPT was developed in close collaboration with marine scientists to enable researchers to explore and analyze the FathomNet image database. FathomGPT provides a custom information retrieval pipeline that leverages OpenAI’s large language models to enable: the creation of complex queries to retrieve images, taxonomic information, and scientific measurements; mapping common names and morphological features to scientific names; generating interactive charts on demand; and searching by image or specified patterns within an image. In designing FathomGPT, particular emphasis was placed on enhancing the user’s experience by facilitating free-form exploration and optimizing response times. We present an architectural overview and implementation details of FathomGPT, along with a series of ablation studies that demonstrate the effectiveness of our approach to name resolution, fine tuning, and prompt modification. We also present usage scenarios of interactive data exploration sessions and document feedback from ocean scientists and machine learning experts. Nabin Khanal, Chun Meng Yu, Jui-Cheng Chiu, Anav Chaudhary, Kakani Katija, Angus G. Forbes |
UIST | 1 |