# Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

arXiv: https://arxiv.org/abs/1708.05045 · HTML: https://mlsanity.com/paper/1708.05045/

## Key facts

- Cited by 127 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2017.

## Built on by (most cited first, 30 of 127)

- [Search to aggregate neighborhood for graph neural network](https://mlsanity.com/paper/2104.06608/) (2021)
- [Nonparametric Masked Language Modeling](https://mlsanity.com/paper/2212.01349/) (2022)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining](https://mlsanity.com/paper/2103.15452/) (2021)
- [SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs](https://mlsanity.com/paper/2203.01044/) (2022)
- [A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning](https://mlsanity.com/paper/2103.15059/) (2021)
- [MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid](https://mlsanity.com/paper/2212.14454/) (2022)
- [RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment](https://mlsanity.com/paper/2103.00791/) (2021)
- [From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment](https://mlsanity.com/paper/2109.02363/) (2021)
- [Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment](https://mlsanity.com/paper/2307.16210/) (2023)
- [Knowing the No-match: Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2106.02248/) (2021)
- [ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities](https://mlsanity.com/paper/2205.10312/) (2022)
- [OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding](https://mlsanity.com/paper/2105.07688/) (2021)
- [Multi-modal Entity Alignment in Hyperbolic Space](https://mlsanity.com/paper/2106.03619/) (2021)
- [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)
- [Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs](https://mlsanity.com/paper/2203.02150/) (2022)
- [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)
- [Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment](https://mlsanity.com/paper/2104.08804/) (2021)
- [Meta-Path Learning for Multi-relational Graph Neural Networks](https://mlsanity.com/paper/2309.17113/) (2023)
- [PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment](https://mlsanity.com/paper/2505.21366/) (2025)
- [Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness](https://mlsanity.com/paper/2108.05278/) (2021)
- [Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding](https://mlsanity.com/paper/2208.11125/) (2022)
- [Informed Multi-context Entity Alignment](https://mlsanity.com/paper/2201.00304/) (2022)
- [Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs](https://mlsanity.com/paper/2207.11436/) (2022)
- [Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets](https://mlsanity.com/paper/2304.03468/) (2023)
- [Revisit and Outstrip Entity Alignment: A Perspective of Generative Models](https://mlsanity.com/paper/2305.14651/) (2023)
- [Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment](https://mlsanity.com/paper/2401.16960/) (2024)
- [Neuro-Symbolic Entity Alignment via Variational Inference](https://mlsanity.com/paper/2410.04153/) (2024)

## Abstract (arXiv)

Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.