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Huang Peng

dblp:202/3225 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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.

Databases, data mining, and information retrieval
1 paper
Knowledge graphs · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge graphs
knowledge graph alignment
0.912025
IKGA: An Interactive Visualization Tool for Knowledge Graph Alignment · ICDE 2025
Visualization and visual analytics
visual analytics
0.912025
IKGA: An Interactive Visualization Tool for Knowledge Graph Alignment · ICDE 2025

Methods — techniques the papers use, named apart from their topics

representation learning · 1.7alignment inference · 1.7
YearPublicationVenuePosition
2025 IKGA: An Interactive Visualization Tool for Knowledge Graph Alignment
abstract
Knowledge Graph Alignment (KGA) identifies elements in different knowledge graphs (KGs) that refer to the same real-world object. It is a key step towards improving the coverage of KGs, which in turn can better facilitate downstream tasks. However, there is currently no interactive tool to support KGA research, particularly for visualizing alignment results, hence limiting the understanding of the procedure and also the development of more advanced solutions. To fill in this gap, in this paper, we introduce IKGA, an interactive visualization tool for KGA, which visualizes the alignment process by integrating various algorithms of representation learning and alignment inference-two key steps in KGA. The system allows users to observe KGA results, compare existing algorithms, and apply to domain-specific KGs at hand.
Weixin Zeng, Shiqi Zhang 0011, Huang Peng, Weidong Xiao 0003, Xiang Zhao 0002
ICDE3
2024 Open knowledge base canonicalization with multi-task learning
Huang Peng, Weixin Zeng, Xiang Zhao 0002, Shijun Liu, Li Pan 0001
World Wide Web (WWW)2