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Hoang-An Trieu

dblp:410/5307 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › relation extraction
joint entity and relation extraction
1.012026
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
1.012026
Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFs · AAAI 2026
Visualization and visual analytics
interactive visualization
0.312026
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
YearPublicationVenuePosition
2026 Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFs
abstract
Scientific 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
AAAI2