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Found 10 papers, 5 papers with code
Deep Reinforcement Learning for Entity Alignment
Embedding-based methods have attracted increasing attention in recent entity alignment (EA) studies.
Unleashing the Power of Transformer for Graphs
Our experiments demonstrate DET has achieved superior performance compared to the respective state-of-the-art methods in dealing with molecules, networks and knowledge graphs with various sizes.
Principled Representation Learning for Entity Alignment
Embedding-based entity alignment (EEA) has recently received great attention.
Towards Principled Representation Learning for Entity Alignment
In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning.
Decentralized Knowledge Graph Representation Learning
Knowledge graph (KG) representation learning methods have achieved competitive performance in many KG-oriented tasks, among which the best ones are usually based on graph neural networks (GNNs), a powerful family of networks that learns the representation of an entity by aggregating the features of its neighbors and itself.
TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs
We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity 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
Moreover, triple-level learning is insufficient for the propagation of semantic information among entities, especially for the case of cross-KG embedding.
Recurrent Skipping Networks for Entity Alignment
We consider the problem of learning knowledge graph (KG) embeddings for entity alignment (EA).
DSKG: A Deep Sequential Model for Knowledge Graph Completion
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of $(subject, relation, object)$.
