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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 81–90 of 151 papers

TitleStatusHype
Neural Relation Extraction for Knowledge Base Enrichment—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph—0
“Politeness, you simpleton!” retorted [MASK]: Masked prediction of literary characters—0
Principled Representation Learning for Entity Alignment—0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification—0
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding—0
SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment—0
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction—0
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