Papers with inductive KGC
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Text-based methods lag behind graph embedding-based approaches for knowledge graph completion (KGC) |
| Approach: | They propose three types of negatives to improve contrastive learning to improve learning efficiency. |
| Outcome: | The proposed model outperforms embedding-based methods on several benchmark datasets. |
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)
Copied to clipboard
| 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. |