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January 1, 2020Open Access

Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models

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Resumen

Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.

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Cite This Study

Zhang et al. (Wed,) studied this question.

synapsesocial.com/papers/6a153f165347fbb1739f77dd https://doi.org/https://doi.org/10.18653/v1/2020.findings-emnlp.25

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1KG-BERT: BERT for Knowledge Graph Completion2019 · 381 citations
2SimplE embedding for link prediction in knowledge graphs2018 · 178 citations
3RotatE: Knowledge Graph Embedding by Relational Rotation in Complex\n Space2019 · 751 citations
4Convolutional 2D Knowledge Graph Embeddings2017 · 733 citations
5Freebase2008 · 4,990 citations

Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models | Synapse