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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 31–40 of 151 papers

TitleStatusHype
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion—0
DERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs—0
Content-Based Personalized Recommender System Using Entity Embeddings—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
A Joint Training Framework for Open-World Knowledge Graph Embeddings—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification—0
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
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale—0
Graph Neural Pre-training for Enhancing Recommendations using Side Information—0
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