# LargeEA: Aligning Entities for Large-scale Knowledge Graphs

arXiv: https://arxiv.org/abs/2108.05211 · HTML: https://mlsanity.com/paper/2108.05211/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2021.

## Built on by (most cited first, 26 of 26)

- [ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities](https://mlsanity.com/paper/2205.10312/) (2022)
- [Unsupervised Entity Alignment for Temporal Knowledge Graphs](https://mlsanity.com/paper/2302.00796/) (2023)
- [LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation](https://mlsanity.com/paper/2210.10436/) (2022)
- [Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport](https://mlsanity.com/paper/2301.12721/) (2023)
- [Can LLMs be Good Graph Judge for Knowledge Graph Construction?](https://mlsanity.com/paper/2411.17388/) (2024)
- [CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations](https://mlsanity.com/paper/2207.05324/) (2022)
- [Knowledge Graph Embedding: An Overview](https://mlsanity.com/paper/2309.12501/) (2023)
- [Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2404.00942/) (2024)
- [Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding](https://mlsanity.com/paper/2208.11125/) (2022)
- [Ensemble Semi-supervised Entity Alignment via Cycle-teaching](https://mlsanity.com/paper/2203.06308/) (2022)
- [Deep Active Alignment of Knowledge Graph Entities and Schemata](https://mlsanity.com/paper/2304.04389/) (2023)
- [Pay More Attention to Relation Exploration for Knowledge Base Question Answering](https://mlsanity.com/paper/2305.02118/) (2023)
- [Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction](https://mlsanity.com/paper/2305.03503/) (2023)
- [SEA: A Scalable Entity Alignment System](https://mlsanity.com/paper/2304.07065/) (2023)
- [High-quality Task Division for Large-scale Entity Alignment](https://mlsanity.com/paper/2208.10366/) (2022)
- [Real-time Workload Pattern Analysis for Large-scale Cloud Databases](https://mlsanity.com/paper/2307.02626/) (2023)
- [Retail-786k: a Large-Scale Dataset for Visual Entity Matching](https://mlsanity.com/paper/2309.17164/) (2023)
- [Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths](https://mlsanity.com/paper/2310.05364/) (2023)
- [Aligning Multiple Knowledge Graphs in a Single Pass](https://mlsanity.com/paper/2408.00662/) (2024)
- [ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training](https://mlsanity.com/paper/2501.07078/) (2025)
- [Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model](https://mlsanity.com/paper/2512.07232/) (2025)
- [Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation](https://mlsanity.com/paper/2402.03025/) (2024)
- [Unsupervised Entity Alignment Based on Personalized Discriminative Rooted Tree](https://mlsanity.com/paper/2502.10044/) (2025)
- [KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.04093/) (2025)
- [Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering](https://mlsanity.com/paper/2606.28076/) (2026)
- [Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passing](https://mlsanity.com/paper/2609.34499/) (2026)

## Abstract (arXiv)

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). Current EA approaches suffer from scalability issues, limiting their usage in real-world EA scenarios. To tackle this challenge, we propose LargeEA to align entities between large-scale KGs. LargeEA consists of two channels, i.e., structure channel and name channel. For the structure channel, we present METIS-CPS, a memory-saving mini-batch generation strategy, to partition large KGs into smaller mini-batches. LargeEA, designed as a general tool, can adopt any existing EA approach to learn entities' structural features within each mini-batch independently. For the name channel, we first introduce NFF, a name feature fusion method, to capture rich name features of entities without involving any complex training process. Then, we exploit a name-based data augmentation to generate seed alignment without any human intervention. Such design fits common real-world scenarios much better, as seed alignment is not always available. Finally, LargeEA derives the EA results by fusing the structural features and name features of entities. Since no widely-acknowledged benchmark is available for large-scale EA evaluation, we also develop a large-scale EA benchmark called DBP1M extracted from real-world KGs. Extensive experiments confirm the superiority of LargeEA against state-of-the-art competitors.