Demo Data from VLDB 2026 / reviewers in the wild / expert
Hoang-An Trieu
dblp:410/5307
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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 |
Knowledge representation and reasoning · 50% Information extraction and text analysis · 50% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › relation extraction
joint entity and relation extraction |
1.0 | 1 | 2026 | Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFs · AAAI 2026 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition
knowledge extraction |
1.0 | 1 | 2026 | Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFs · AAAI 2026 |
Visualization and visual analytics
interactive visualization |
0.3 | 1 | 2026 | Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFs · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
rule-based extraction · 3.0LLM-based extraction · 3.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFsabstractScientific research articles, typically distributed in PDF format, contain valuable knowledge but remain challenging to convert into structured datasets due to fragmented workflows that separate parsing, annotation, and visualization. Existing annotation platforms operate on plain text, which requires an additional PDF-to-text conversion step before annotation, while PDF parsing tools lack automated annotation suggestions. To bridge this gap, we introduce Docora, a system that unifies PDF parsing, automated annotation assistance, and multi-view visualization into a single interactive platform. Docora enables researchers to configure entity and relation schemas for any domain, automatically generates initial annotations using rule-based, model-based, or LLM-based extractors, and provides synchronized visualizations across PDF, text, and graph views. Users can refine annotations directly on the PDF canvas, ensuring consistency between document layout and structured representations. The system’s source code is publicly available to facilitate further research and development. Dinh-Truong Do, Hoang-An Trieu, Van-Thuy Phi, Minh Le Nguyen 0001, Yuji Matsumoto 0001 |
AAAI | 2 |