Knowledge Graph Fusion
Knowledge Graph Fusion is a computational framework that integrates heterogeneous knowledge graphs by aligning and reconciling overlapping entities, relations, and schemas to produce a unified, coherent knowledge graph while preserving and enhancing semantic meaning and structural integrity.
65
Publications
2.9K
Citations
273
Authors
94
Institutions
Publications per year
2017–2026
63
Parents
65
Knowledge Graphs: Opportunities and Challenges
Ciyuan Peng, Feng Xia, Mehdi Naseriparsa et al. · Artificial Intelligence Review · 2023 · 604 citations · Full text
Dynamically Fused Graph Network for Multi-hop Reasoning
Lin Qiu, Yunxuan Xiao, Yanru Qu et al. · 2019 · 180 citations · Full text
A comprehensive survey of entity alignment for knowledge graphs
Kaisheng Zeng, Chengjiang Li, Lei Hou et al. · AI Open · 2021 · 125 citations · Full text
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94

Chinese Academy of Sciences
Beijing, China
7 h-index
18 publications
450 citations
University of Chinese Academy of Sciences
Beijing, China
5 h-index
10 publications
215 citations

Peking University
Beijing, China
4 h-index
13 publications
807 citations
Tsinghua University
Beijing, China
4 h-index
13 publications
770 citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
5 publications
125 citations
Tencent (China)
Shenzhen, China
3 h-index
5 publications
150 citations
Wuhan University
Wuhan, China
3 h-index
4 publications
104 citations
Tongji University
Shanghai, China
2 h-index
8 publications
332 citations
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4 publications · 71 citations
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TKDE · 3 publications · 114 citations
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Repository · 3 publications · 76 citations
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3 publications · 227 citations
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Conference · 3 publications · 172 citations
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