Papers by Pengjiang Qian
Joint Pre-Encoding Representation and Structure Embedding for Efficient and Low-Resource Knowledge Graph Completion (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge graph completion models require longer training and inference times as well as increased memory usage. |
| Approach: | They propose to encode textual descriptions into semantic representations before training and integrate structural embedding with pre-encoded semantic description to improve model's prediction performance on 1-N relations. |
| Outcome: | The proposed model increases inference speed by 30x and reduces training memory by approximately 60% on the WN18RR and UMLS datasets. |