# High-quality Task Division for Large-scale Entity Alignment

arXiv: https://arxiv.org/abs/2208.10366 · HTML: https://mlsanity.com/paper/2208.10366/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs](https://mlsanity.com/paper/2003.07743/) (2020)
- [Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/1908.08210/) (2019)
- [A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions](https://mlsanity.com/paper/2105.11644/) (2021)
- [Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment](https://mlsanity.com/paper/2010.03249/) (2020)
- [Relational Reflection Entity Alignment](https://mlsanity.com/paper/2008.07962/) (2020)
- [Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment](https://mlsanity.com/paper/1806.06478/) (2018)
- [Knowledge Association with Hyperbolic Knowledge Graph Embeddings](https://mlsanity.com/paper/2010.02162/) (2020)
- [LargeEA: Aligning Entities for Large-scale Knowledge Graphs](https://mlsanity.com/paper/2108.05211/) (2021)
- [Relation-Aware Neighborhood Matching Model for Entity Alignment](https://mlsanity.com/paper/2012.08128/) (2020)
- [ActiveEA: Active Learning for Neural Entity Alignment](https://mlsanity.com/paper/2110.06474/) (2021)
- [Ensemble Semi-supervised Entity Alignment via Cycle-teaching](https://mlsanity.com/paper/2203.06308/) (2022)

## Built on by (most cited first, 4 of 4)

- [Unsupervised Entity Alignment for Temporal Knowledge Graphs](https://mlsanity.com/paper/2302.00796/) (2023)
- [Guiding Neural Entity Alignment with Compatibility](https://mlsanity.com/paper/2211.15833/) (2022)
- [Dependency-aware Self-training for Entity Alignment](https://mlsanity.com/paper/2211.16101/) (2022)
- [Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model](https://mlsanity.com/paper/2512.07232/) (2025)

## Abstract (arXiv)

Entity Alignment (EA) aims to match equivalent entities that refer to the same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most neural EA models cannot be applied to large-scale real-life KGs due to their excessive consumption of GPU memory and time. One promising solution is to divide a large EA task into several subtasks such that each subtask only needs to match two small subgraphs of the original KGs. However, it is challenging to divide the EA task without losing effectiveness. Existing methods display low coverage of potential mappings, insufficient evidence in context graphs, and largely differing subtask sizes. In this work, we design the DivEA framework for large-scale EA with high-quality task division. To include in the EA subtasks a high proportion of the potential mappings originally present in the large EA task, we devise a counterpart discovery method that exploits the locality principle of the EA task and the power of trained EA models. Unique to our counterpart discovery method is the explicit modelling of the chance of a potential mapping. We also introduce an evidence passing mechanism to quantify the informativeness of context entities and find the most informative context graphs with flexible control of the subtask size. Extensive experiments show that DivEA achieves higher EA performance than alternative state-of-the-art solutions.