Corpus ID: 235458246

A Self-supervised Method for Entity Alignment

@article{Liu2021ASM,
  title={A Self-supervised Method for Entity Alignment},
  author={Xiao Liu and Haoyun Hong and Xinghao Wang and Zeyi Chen and E. Kharlamov and Yuxiao Dong and Jie Tang},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09395}
}
Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing large-scale KGs. Over the course of its development, supervision has been considered necessary for accurate alignments. Inspired by the recent progress of self-supervised learning, we explore the extent to which we can get rid of supervision for entity alignment. Existing supervised methods for this task focus on pulling each pair of positive (labeled… Expand

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References

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TLDR
This paper presents a novel joint learning framework for entity alignment that is a Graph Convolutional Network (GCN) based framework for learning both entity and relation representations and incorporates the relation approximation into entities to iteratively learn better representations for both. Expand
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TLDR
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TLDR
This work proposes COTSAE that combines the structure and attribute information of entities by co-training two embedding learning components, respectively, and proposes a joint attention method in the model to learn the attentions of attribute types and values cooperatively. Expand
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TLDR
This paper proposes a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG), which can make better use of seed alignments to propagate over the entire graphs with KG-based constraints. Expand
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