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.
Benchmarks
These leaderboards are used to track progress in Entity Alignment
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DBP15K
UMVM
EventEA
Most implemented papers
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
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
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ICLR 2020
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Relational Reflection Entity Alignment
MaoXinn/RREA
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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
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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
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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
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
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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
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In this paper, we present the first framework that can generate explanations for understanding and repairing embedding-based EA results.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
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.
ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
zjukg/MEAformer
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Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects.
