Papers by Junpeng Bao

1 papers
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)

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Challenge: Existing knowledge graph embedding methods cannot capture local and global information and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs.
Approach: They propose a double-branch multi-attention based graph neural network to learn more expressive entity representations which contain rich global-local structural information.
Outcome: The proposed method outperforms a general GNN-based approach for KGC.

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