Graph Embedding Design
Graph Embedding Design is a methodological framework within Knowledge Graph Design that formally specifies the construction and optimization of low-dimensional vector representations of graph entities and relations, distinguished by its focus on preserving structural and semantic properties of the knowledge graph for downstream machine learning tasks.
50
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5.3K
Citations
182
Authors
82
Institutions
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2017–2026
36
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Knowledge Graph Embedding for Link Prediction
Andrea Rossi, Denilson Barbosa, Donatella Firmani et al. · ACM Transactions on Knowledge Discovery from Data · 2021 · 476 citations · Full text
Semantically Smooth Knowledge Graph Embedding
Shu Guo, Quan Wang, Bin Wang et al. · 2015 · 154 citations · Full text
Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
Andrea Rossi, Donatella Firmani, Antonio Matinata et al. · arXiv (Cornell University) · 2020 · 141 citations · Full text
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Hong Kong University of Science and Technology, Hong Kong SAR China
3 publications · 121 citations
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1–5 of 182
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Institute of Information Engineering
Beijing, China
4 h-index
14 publications
9.3K citations

Chinese Academy of Sciences
Beijing, China
4 h-index
13 publications
1.3K citations
Tsinghua University
Beijing, China
3 h-index
8 publications
123 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
14 publications
1.7K citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
7 publications
674 citations

Renmin University of China
Beijing, China
2 h-index
6 publications
545 citations

Beijing University of Posts and Telecommunications
Beijing, China
2 h-index
5 publications
142 citations
University of Science and Technology of China
Hefei, China
2 h-index
9 publications
376 citations
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Repository · 4 publications · 212 citations
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4 publications · 366 citations
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Conference · 2 publications · 95 citations
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2 publications · 63 citations
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TKDD · 1 publications · 476 citations
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