# Visual Pivoting for (Unsupervised) Entity Alignment

arXiv: https://arxiv.org/abs/2009.13603 · HTML: https://mlsanity.com/paper/2009.13603/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 30 of 47)

- [Multi-modal Contrastive Representation Learning for Entity Alignment](https://mlsanity.com/paper/2209.00891/) (2022)
- [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)
- [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)
- [Unsupervised Entity Alignment for Temporal Knowledge Graphs](https://mlsanity.com/paper/2302.00796/) (2023)
- [Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment](https://mlsanity.com/paper/2302.08774/) (2023)
- [Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport](https://mlsanity.com/paper/2301.12721/) (2023)
- [Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding](https://mlsanity.com/paper/2208.11125/) (2022)
- [Revisit and Outstrip Entity Alignment: A Perspective of Generative Models](https://mlsanity.com/paper/2305.14651/) (2023)
- [Interactive Contrastive Learning for Self-supervised Entity Alignment](https://mlsanity.com/paper/2201.06225/) (2022)
- [Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer](https://mlsanity.com/paper/2205.08806/) (2022)
- [A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs](https://mlsanity.com/paper/2209.09677/) (2022)
- [Deep Active Alignment of Knowledge Graph Entities and Schemata](https://mlsanity.com/paper/2304.04389/) (2023)
- [Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment](https://mlsanity.com/paper/2310.06365/) (2023)
- [Noise-powered Multi-modal Knowledge Graph Representation Framework](https://mlsanity.com/paper/2403.06832/) (2024)
- [VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment](https://mlsanity.com/paper/2102.04081/) (2021)
- [From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment](https://mlsanity.com/paper/2305.11501/) (2023)
- [Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment](https://mlsanity.com/paper/2403.01203/) (2024)
- [ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment](https://mlsanity.com/paper/2402.11000/) (2024)
- [Progressively Modality Freezing for Multi-Modal Entity Alignment](https://mlsanity.com/paper/2407.16168/) (2024)
- [Dangling-Aware Entity Alignment with Mixed High-Order Proximities](https://mlsanity.com/paper/2205.02406/) (2022)
- [IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment](https://mlsanity.com/paper/2407.19302/) (2024)
- [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)
- [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)
- [Leveraging Intra-modal and Inter-modal Interaction for Multi-Modal Entity Alignment](https://mlsanity.com/paper/2404.17590/) (2024)

## Abstract (arXiv)

This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. Besides, previous entity alignment methods require human labelled seed alignment, restricting availability. EVA provides a completely unsupervised solution by leveraging the visual similarity of entities to create an initial seed dictionary (visual pivots). Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts necessary for capturing the correspondences.