About
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.
Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Benchmarks
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Greatest papers with code
Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Ranked #3 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings.
Multi-view Knowledge Graph Embedding for Entity Alignment
Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs.
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
• nju-websoft/RSN
• 
Moreover, triple-level learning is insufficient for the propagation of semantic information among entities, especially for the case of cross-KG embedding.
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
• nju-websoft/JAPE
•
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.
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures.
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods.
Ranked #8 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Neighborhood Matching Network for Entity Alignment
This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge.
Multi-Channel Graph Neural Network for Entity Alignment
ACL 2019
• thunlp/MuGNN
• 
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments.
Ranked #14 on
Entity Alignment
on DBP15k zh-en
Iterative Entity Alignment via Joint Knowledge Embeddings
During this process, we can align entities according to their semantic distance in this joint semantic space.