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
Iterative Entity Alignment via Joint Knowledge Embeddings
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
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.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments.
SecureBoost: A Lossless Federated Learning Framework
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs
Moreover, triple-level learning is insufficient for the propagation of semantic information among entities, especially for the case of cross-KG embedding.
Multi-view Knowledge Graph Embedding for Entity Alignment
Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs.
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.
Multi-Channel Graph Neural Network for Entity Alignment
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments.
Jointly Learning Entity and Relation Representations for Entity Alignment
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs).