Entity Alignment

86 papers with code • 10 benchmarks • 7 datasets

Entity Alignment is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used extra training labels (both are marked with "Extra Training Data" in the leaderboard) and are not adhere to a comparable setting with others that have followed the original setting of the benchmark.

Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

The task of entity alignment is related to the task of entity resolution which focuses on matching structured entity descriptions in different contexts.

Most implemented papers

Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

muhaochen/MTransE 12 Nov 2016

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs.

Deep Graph Matching Consensus

rusty1s/deep-graph-matching-consensus ICLR 2020

This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.

Relational Reflection Entity Alignment

MaoXinn/RREA 18 Aug 2020

Entity alignment aims to identify equivalent entity pairs from different Knowledge Graphs (KGs), which is essential in integrating multi-source KGs.

Visual Pivoting for (Unsupervised) Entity Alignment

cambridgeltl/eva 28 Sep 2020

This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs).

RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment

zhurboo/RAGA 1 Mar 2021

Entity alignment (EA) is the task to discover entities referring to the same real-world object from different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs.

ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities

joker-xii/clusterea 20 May 2022

To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation

THU-KEG/Entity_Alignment_Papers 19 Oct 2022

Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs.

Iterative Entity Alignment via Joint Knowledge Embeddings

thunlp/IEAJKE International Joint Conference on Artificial Intelligence 2017

During this process, we can align entities according to their semantic distance in this joint semantic space.

Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

nju-websoft/JAPE 16 Aug 2017

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.