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Artículo
TAPON: a two-phase machine learning approach for semantic labelling
(Elsevier, 2019-01-01)
Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the ...
Artículo
TAPON-MT: a versatile framework for semantic labelling
(Elsevier, 2019-07)
Semantic labelling refers to the problem of assigning known labels to the elements of structured information from a source such as an HTML table or an RDF dump with unknown semantics. In the recent years it has become ...
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 ...