# Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

arXiv: https://arxiv.org/abs/2001.08728 · HTML: https://mlsanity.com/paper/2001.08728/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 15 of 15)

- [Differentially Private Federated Knowledge Graphs Embedding](https://mlsanity.com/paper/2105.07615/) (2021)
- [A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning](https://mlsanity.com/paper/2103.15059/) (2021)
- [RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment](https://mlsanity.com/paper/2103.00791/) (2021)
- [LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation](https://mlsanity.com/paper/2210.10436/) (2022)
- [Type-Driven Multi-Turn Corrections for Grammatical Error Correction](https://mlsanity.com/paper/2203.09136/) (2022)
- [Revisit and Outstrip Entity Alignment: A Perspective of Generative Models](https://mlsanity.com/paper/2305.14651/) (2023)
- [Reinforcement Learning based Collective Entity Alignment with Adaptive Features](https://mlsanity.com/paper/2101.01353/) (2021)
- [An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection](https://mlsanity.com/paper/2203.05147/) (2022)
- [Towards Entity Alignment in the Open World: An Unsupervised Approach](https://mlsanity.com/paper/2101.10535/) (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)
- [Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding](https://mlsanity.com/paper/2401.12798/) (2024)
- [NALA: an Effective and Interpretable Entity Alignment Method](https://mlsanity.com/paper/2404.11968/) (2024)
- [Graph Neural Networks for Natural Language Processing: A Survey](https://mlsanity.com/paper/2106.06090/) (2021)
- [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026)

## Abstract (arXiv)

Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding method may not only cause the "many-to-one" problem but also neglect the coordinated nature of this task, that is, each alignment decision may highly correlate to the other decisions. In this paper, we introduce two coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and joint entity alignment algorithm. Specifically, the Easy-to-Hard strategy first retrieves the model-confident alignments from the predicted results and then incorporates them as additional knowledge to resolve the remaining model-uncertain alignments. To achieve this, we further propose an enhanced alignment model that is built on the current state-of-the-art baseline. In addition, to address the many-to-one problem, we propose to jointly predict entity alignments so that the one-to-one constraint can be naturally incorporated into the alignment prediction. Experimental results show that our model achieves the state-of-the-art performance and our reasoning methods can also significantly improve existing baselines.