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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 76–100 of 151 papers

TitleStatusHype
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings—0
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models—0
Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis—0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities—0
Neural Entity Linking: A Survey of Models Based on Deep Learning—0
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
SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs—0
Supervised Typing of Big Graphs using Semantic Embeddings—0
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework—0
TorusE: Knowledge Graph Embedding on a Lie Group—0
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs—0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment—0
Understanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237—0
Universal Embeddings of Tabular Data—0
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers—0
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