Knowledge Graph Embedding Bi-Vector Models for Symmetric Relation

@article{Yao2019KnowledgeGE,
  title={Knowledge Graph Embedding Bi-Vector Models for Symmetric Relation},
  author={Jinkui Yao and Liang Xu},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.09557}
}
Knowledge graph embedding (KGE) models have been proposed to improve the performance of knowledge graph reasoning. However, there is a general phenomenon in most of KGEs, as the training progresses, the symmetric relations tend to zero vector, if the symmetric triples ratio is high enough in the dataset. This phenomenon causes subsequent tasks, e.g. link prediction etc., of symmetric relations to fail. The root cause of the problem is that KGEs do not utilize the semantic information of… 
1 Citations
A Survey on Knowledge Graph Embeddings for Link Prediction
TLDR
A comprehensive survey on KG-embedding models for link prediction in knowledge graphs is provided and a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding are investigated.

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