# SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

arXiv: https://arxiv.org/abs/2203.01044 · HTML: https://mlsanity.com/paper/2203.01044/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 56 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1611.03954/) (2016)
- [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding](https://mlsanity.com/paper/1708.05045/) (2017)
- [Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/1908.08210/) (2019)
- [Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation](https://mlsanity.com/paper/1911.08936/) (2019)
- [Multi-Channel Graph Neural Network for Entity Alignment](https://mlsanity.com/paper/1908.09898/) (2019)
- [Deep Graph Matching Consensus](https://mlsanity.com/paper/2001.09621/) (2020)
- [Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs](https://mlsanity.com/paper/1905.04914/) (2019)
- [Multi-view Knowledge Graph Embedding for Entity Alignment](https://mlsanity.com/paper/1906.02390/) (2019)
- [Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network](https://mlsanity.com/paper/1905.11605/) (2019)
- [Aligning Cross-Lingual Entities with Multi-Aspect Information](https://mlsanity.com/paper/1910.06575/) (2019)
- [TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs](https://mlsanity.com/paper/2004.13579/) (2020)
- [Jointly Learning Entity and Relation Representations for Entity Alignment](https://mlsanity.com/paper/1909.09317/) (2019)
- [Relation-Aware Neighborhood Matching Model for Entity Alignment](https://mlsanity.com/paper/2012.08128/) (2020)
- [JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment](https://mlsanity.com/paper/1910.13105/) (2019)
- [AliMe KG: Domain Knowledge Graph Construction and Application in E-commerce](https://mlsanity.com/paper/2009.11684/) (2020)

## Built on by (most cited first, 30 of 32)

- [GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner](https://mlsanity.com/paper/2304.04779/) (2023)
- [A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment](https://mlsanity.com/paper/2305.06574/) (2023)
- [Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport](https://mlsanity.com/paper/2301.12721/) (2023)
- [PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking](https://mlsanity.com/paper/2405.07500/) (2024)
- [HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting](https://mlsanity.com/paper/2304.05973/) (2023)
- [A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective](https://mlsanity.com/paper/2403.16137/) (2024)
- [From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment](https://mlsanity.com/paper/2305.11501/) (2023)
- [Deep Outdated Fact Detection in Knowledge Graphs](https://mlsanity.com/paper/2402.03732/) (2024)
- [Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment](https://mlsanity.com/paper/2403.01203/) (2024)
- [Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment](https://mlsanity.com/paper/2209.01847/) (2022)
- [Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2303.14898/) (2023)
- [What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings](https://mlsanity.com/paper/2306.02622/) (2023)
- [Neural Machine Translation with Dynamic Graph Convolutional Decoder](https://mlsanity.com/paper/2305.17698/) (2023)
- [Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases](https://mlsanity.com/paper/2403.10978/) (2024)
- [Pseudo Contrastive Learning for Graph-based Semi-supervised Learning](https://mlsanity.com/paper/2302.09532/) (2023)
- [Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment](https://mlsanity.com/paper/2307.02075/) (2023)
- [Aligning Multiple Knowledge Graphs in a Single Pass](https://mlsanity.com/paper/2408.00662/) (2024)
- [Investigating Graph Structure Information for Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2304.04718/) (2023)
- [Unsupervised Deep Cross-Language Entity Alignment](https://mlsanity.com/paper/2309.10598/) (2023)
- [NALA: an Effective and Interpretable Entity Alignment Method](https://mlsanity.com/paper/2404.11968/) (2024)
- [HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption](https://mlsanity.com/paper/2301.12063/) (2023)
- [Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation](https://mlsanity.com/paper/2402.03025/) (2024)
- [Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings](https://mlsanity.com/paper/2405.10745/) (2024)
- [DERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs](https://mlsanity.com/paper/2408.01154/) (2024)
- [Unsupervised Entity Alignment Based on Personalized Discriminative Rooted Tree](https://mlsanity.com/paper/2502.10044/) (2025)
- [FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic](https://mlsanity.com/paper/2510.20467/) (2025)
- [PSQE: A Theoretical-Practical Approach to Pseudo Seed Quality Enhancement for Unsupervised Multimodal Entity Alignment](https://mlsanity.com/paper/2602.22903/) (2026)
- [Towards Effective and Efficient Graph Alignment without Supervision](https://mlsanity.com/paper/2603.08526/) (2026)
- [Graph Fusion Across Languages using Large Language Models](https://mlsanity.com/paper/2603.21248/) (2026)
- [Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate](https://mlsanity.com/paper/2604.13551/) (2026)

## Abstract (arXiv)

Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its development, the label 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. Commonly, the label information (positive entity pairs) is used to supervise the process of pulling the aligned entities in each positive pair closer. However, our theoretical analysis suggests that the learning of entity alignment can actually benefit more from pushing unlabeled negative pairs far away from each other than pulling labeled positive pairs close. By leveraging this discovery, we develop the self-supervised learning objective for entity alignment. We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision. Extensive experiments on benchmark datasets demonstrate that SelfKG without supervision can match or achieve comparable results with state-of-the-art supervised baselines. The performance of SelfKG suggests that self-supervised learning offers great potential for entity alignment in KGs. The code and data are available at https://github.com/THUDM/SelfKG.