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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 126–150 of 151 papers

TitleStatusHype
Abstract Graphs and Abstract Paths for Knowledge Graph Completion—0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural NetworkCode0
Multi-relational Poincaré Graph EmbeddingsCode0
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding—0
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning—0
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
Incorporating Literals into Knowledge Graph EmbeddingsCode0
DeepType: Multilingual Entity Linking by Neural Type System EvolutionCode0
TorusE: Knowledge Graph Embedding on a Lie Group—0
Convolutional Neural Knowledge Graph Learning—0
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings—0
Named Entity Disambiguation for Noisy TextCode0
Apprendre des repr\'esentations jointes de mots et d'entit\'es pour la d\'esambigu\" d'entit\'es (Combining Word and Entity Embeddings for Entity Linking)—0
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsCode0
Supervised Typing of Big Graphs using Semantic Embeddings—0
DAWT: Densely Annotated Wikipedia Texts across multiple languages—0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities—0
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data—0
Entity Embeddings of Categorical VariablesCode0
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