# RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment

arXiv: https://arxiv.org/abs/2103.00791 · HTML: https://mlsanity.com/paper/2103.00791/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [K-BERT: Enabling Language Representation with Knowledge Graph](https://mlsanity.com/paper/1909.07606/) (2019)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [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)
- [Deep Graph Matching Consensus](https://mlsanity.com/paper/2001.09621/) (2020)
- [Relational Reflection Entity Alignment](https://mlsanity.com/paper/2008.07962/) (2020)
- [Neighborhood Matching Network for Entity Alignment](https://mlsanity.com/paper/2005.05607/) (2020)
- [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)
- [Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment](https://mlsanity.com/paper/2001.08728/) (2020)
- [Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation](https://mlsanity.com/paper/2007.13207/) (2020)

## Built on by (most cited first, 29 of 29)

- [Revisit and Outstrip Entity Alignment: A Perspective of Generative Models](https://mlsanity.com/paper/2305.14651/) (2023)
- [An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection](https://mlsanity.com/paper/2203.05147/) (2022)
- [Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer](https://mlsanity.com/paper/2205.08806/) (2022)
- [Mitigating Relational Bias on Knowledge Graphs](https://mlsanity.com/paper/2211.14489/) (2022)
- [EchoEA: Echo Information between Entities and Relations for Entity Alignment](https://mlsanity.com/paper/2107.03054/) (2021)
- [Deep Reinforcement Learning for Entity Alignment](https://mlsanity.com/paper/2203.03315/) (2022)
- [Semi-constraint Optimal Transport for Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2203.05744/) (2022)
- [Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification](https://mlsanity.com/paper/2312.07338/) (2023)
- [Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding](https://mlsanity.com/paper/2401.12798/) (2024)
- [Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning](https://mlsanity.com/paper/2412.05028/) (2024)
- [Unsupervised Deep Cross-Language Entity Alignment](https://mlsanity.com/paper/2309.10598/) (2023)
- [Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model](https://mlsanity.com/paper/2512.07232/) (2025)
- [Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition](https://mlsanity.com/paper/2106.04392/) (2021)
- [Cybersecurity Entity Alignment via Masked Graph Attention Networks](https://mlsanity.com/paper/2207.01434/) (2022)
- [Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment](https://mlsanity.com/paper/2305.01556/) (2023)
- [OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment](https://mlsanity.com/paper/2305.01561/) (2023)
- [Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs](https://mlsanity.com/paper/2307.11206/) (2023)
- [Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter](https://mlsanity.com/paper/2309.09443/) (2023)
- [GMTR: Graph Matching Transformers](https://mlsanity.com/paper/2311.08141/) (2023)
- [DarkShot: Lighting Dark Images with Low-Compute and High-Quality](https://mlsanity.com/paper/2312.16805/) (2023)
- [TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing](https://mlsanity.com/paper/2401.05431/) (2024)
- [TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks](https://mlsanity.com/paper/2401.05432/) (2024)
- [T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image](https://mlsanity.com/paper/2403.13663/) (2024)
- [Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts](https://mlsanity.com/paper/2407.15588/) (2024)
- [Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithms](https://mlsanity.com/paper/2408.17101/) (2024)
- [Towards General Deepfake Detection with Dynamic Curriculum](https://mlsanity.com/paper/2410.11162/) (2024)
- [Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2410.13409/) (2024)
- [Mask6D: Masked Pose Priors For 6D Object Pose Estimation](https://mlsanity.com/paper/2507.06486/) (2025)
- [Charting the Design Space of Neural Graph Representations for Subgraph Matching](https://mlsanity.com/paper/2510.22897/) (2025)

## Abstract (arXiv)

Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. The majority of the existing embeddings-based entity alignment methods embed entities and relations into a vector space based on relation triples of KGs for local alignment. As these methods insufficiently consider the multiple relations between entities, the structure information of KGs has not been fully leveraged. In this paper, we propose a novel framework based on Relation-aware Graph Attention Networks to capture the interactions between entities and relations. Our framework adopts the self-attention mechanism to spread entity information to the relations and then aggregate relation information back to entities. Furthermore, we propose a global alignment algorithm to make one-to-one entity alignments with a fine-grained similarity matrix. Experiments on three real-world cross-lingual datasets show that our framework outperforms the state-of-the-art methods.