Papers with KGBERT

1 papers
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)

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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.

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