# Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment

arXiv: https://arxiv.org/abs/2010.03249 · HTML: https://mlsanity.com/paper/2010.03249/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 30 of 63)

- [Multi-modal Contrastive Representation Learning for Entity Alignment](https://mlsanity.com/paper/2209.00891/) (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)
- [From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment](https://mlsanity.com/paper/2109.02363/) (2021)
- [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)
- [A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment](https://mlsanity.com/paper/2305.06574/) (2023)
- [Unlocking the Power of Large Language Models for Entity Alignment](https://mlsanity.com/paper/2402.15048/) (2024)
- [ActiveEA: Active Learning for Neural Entity Alignment](https://mlsanity.com/paper/2110.06474/) (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)
- [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)
- [AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models](https://mlsanity.com/paper/2307.11772/) (2023)
- [Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment](https://mlsanity.com/paper/2310.06365/) (2023)
- [From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment](https://mlsanity.com/paper/2305.11501/) (2023)
- [ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment](https://mlsanity.com/paper/2402.11000/) (2024)
- [Dangling-Aware Entity Alignment with Mixed High-Order Proximities](https://mlsanity.com/paper/2205.02406/) (2022)
- [High-quality Task Division for Large-scale Entity Alignment](https://mlsanity.com/paper/2208.10366/) (2022)
- [Improving Knowledge Graph Entity Alignment with Graph Augmentation](https://mlsanity.com/paper/2304.14585/) (2023)
- [IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment](https://mlsanity.com/paper/2407.19302/) (2024)
- [Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment](https://mlsanity.com/paper/2201.11249/) (2022)
- [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)
- [Exploring 360-Degree View of Customers for Lookalike Modeling](https://mlsanity.com/paper/2304.09105/) (2023)

## Abstract (arXiv)

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performances by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective evaluation, we propose a hard experimental setting where we select equivalent entity pairs with very different names as the test set. Under both the regular and hard settings, our method achieves significant improvements (5.10% on average Hits@1 in DBP15k) over 12 baselines in cross-lingual and monolingual datasets. Ablation studies on different subgraphs and a case study about attribute types further demonstrate the effectiveness of our method. Source code and data can be found at https://github.com/thunlp/explore-and-evaluate.