Self-supervised entity alignment (EA) aims to link equivalent entities across
different knowledge graphs (KGs) without seed alignments. The current SOTA
self-supervised EA method draws inspiration from contrastive learning,
originally designed in computer vision based on instance discrimination and
contrastive loss, and suffers from two shortcomings. Firstly, it puts
unidirectional emphasis on pushing sampled negative entities far away rather
than pulling positively aligned pairs close, as is done in the well-established
supervised EA. Secondly, KGs contain rich side information (e.g., entity
description), and how to effectively leverage those information has not been
adequately investigated in self-supervised EA. In this paper, we propose an
interactive contrastive learning model for self-supervised EA. The model
encodes not only structures and semantics of entities (including entity name,
entity description, and entity neighborhood), but also conducts cross-KG
contrastive learning by building pseudo-aligned entity pairs. Experimental
results show that our approach outperforms previous best self-supervised
results by a large margin (over 9% average improvement) and performs on par
with previous SOTA supervised counterparts, demonstrating the effectiveness of
the interactive contrastive learning for self-supervised EA.

本篇论文提出了交互式对比学习模型，不仅编码了实体的结构和语义信息（包括实体名称、实体描述和实体邻域），还通过构建伪对齐实体对进行跨知识图谱的对比学习，展示出该模型有效性较高，并在实验中超越了之前最佳的自监督结果（平均提高了 9％以上），且性能与之前的 SOTA 监督方法相当。

交互式对比学习用于自监督实体对齐

Interactive Contrastive Learning for Self-supervised Entity Alignment

We present a collaborative learning method called Mutual Contrastive Learning
(MCL) for general visual representation learning. The core idea of MCL is to
perform mutual interaction and transfer of contrastive distributions among a
cohort of networks. A crucial component of MCL is Interactive Contrastive
Learning (ICL). Compared with vanilla contrastive learning, ICL can aggregate
cross-network embedding information and maximize the lower bound to the mutual
information between two networks. This enables each network to learn extra
contrastive knowledge from others, leading to better feature representations
for visual recognition tasks. We emphasize that the resulting MCL is
conceptually simple yet empirically powerful. It is a generic framework that
can be applied to both supervised and self-supervised representation learning.
Experimental results on image classification and transfer learning to object
detection show that MCL can lead to consistent performance gains, demonstrating
that MCL can guide the network to generate better feature representations. Code
is available at this https URL

Mutual Contrastive Learning (MCL) is a powerful method for improving feature representations for visual recognition tasks through the mutual interaction and transfer of contrastive distributions among a cohort of networks, achieved through the use of Interactive Contrastive Learning (ICL) to aggregate cross-network embedding information.