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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 141–150 of 151 papers

TitleStatusHype
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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