Entity Alignment

106 papers with code • 10 benchmarks • 8 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

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

zjukg/kg-mm-survey 8 Feb 2024

In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.

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.

EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines

SDM-TIB/SDM-RDFizer 14 Dec 2021

Mapping languages, e. g., RML and R2RML, facilitate declarative specification of the process of applying meta-data and integrating data into a knowledge graph.

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.

Generating Explanations to Understand and Repair Embedding-based Entity Alignment

nju-websoft/exea 8 Dec 2023

In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based EA results.