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Entity Embeddings

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.

Papers

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Showing 131–140 of 151 papers

TitleStatusHype
Word Embeddings for Entity-annotated TextsCode0
End-to-End Neural Entity LinkingCode0
Knowledge Representation with Conceptual Spaces—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
ELDEN: Improved Entity Linking Using Densified Knowledge GraphsCode0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
DeepType: Multilingual Entity Linking by Neural Type System EvolutionCode0
Incorporating Literals into Knowledge Graph EmbeddingsCode0
TorusE: Knowledge Graph Embedding on a Lie Group—0
RDF2Vec: RDF Graph Embeddings and Their ApplicationsCode1
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