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Artículo
Deep embeddings and Graph Neural Networks: using context to improve domain-independent predictions
(SprigerLink, 2023-06-28)
Graph neural networks (GNNs) are deep learning architectures that apply graph convolutions through message-passing processes between nodes, represented as embeddings. GNNs have recently become popular because of their ...
Artículo
LEAPME: Learning-based Property Matching with Embeddings
(Cornell University, 2020)
Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their ...