# Multi-view Knowledge Graph Embedding for Entity Alignment

arXiv: https://arxiv.org/abs/1906.02390 · HTML: https://mlsanity.com/paper/1906.02390/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 30 of 74)

- [Differentially Private Federated Knowledge Graphs Embedding](https://mlsanity.com/paper/2105.07615/) (2021)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Multi-modal Contrastive Representation Learning for Entity Alignment](https://mlsanity.com/paper/2209.00891/) (2022)
- [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)
- [SGAligner : 3D Scene Alignment with Scene Graphs](https://mlsanity.com/paper/2304.14880/) (2023)
- [Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment](https://mlsanity.com/paper/2203.14987/) (2022)
- [Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment](https://mlsanity.com/paper/2304.01563/) (2023)
- [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)
- [Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding](https://mlsanity.com/paper/2105.05596/) (2021)
- [Tele-Knowledge Pre-training for Fault Analysis](https://mlsanity.com/paper/2210.11298/) (2022)
- [A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment](https://mlsanity.com/paper/2305.06574/) (2023)
- [KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge](https://mlsanity.com/paper/2405.16412/) (2024)
- [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)
- [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)
- [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)
- [Interactive Contrastive Learning for Self-supervised Entity Alignment](https://mlsanity.com/paper/2201.06225/) (2022)
- [Ensemble Semi-supervised Entity Alignment via Cycle-teaching](https://mlsanity.com/paper/2203.06308/) (2022)
- [Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer](https://mlsanity.com/paper/2205.08806/) (2022)
- [Deep Active Alignment of Knowledge Graph Entities and Schemata](https://mlsanity.com/paper/2304.04389/) (2023)
- [AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models](https://mlsanity.com/paper/2307.11772/) (2023)
- [EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs](https://mlsanity.com/paper/2101.06126/) (2021)
- [Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review](https://mlsanity.com/paper/2203.09280/) (2022)

## Abstract (arXiv)

We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.