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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 101–125 of 151 papers

TitleStatusHype
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings—0
A Joint Training Framework for Open-World Knowledge Graph Embeddings—0
DAWT: Densely Annotated Wikipedia Texts across multiple languages—0
Convolutional Neural Knowledge Graph Learning—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
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion—0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities—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
Neural Entity Linking: A Survey of Models Based on Deep Learning—0
Neural Relation Extraction for Knowledge Base Enrichment—0
Content-Based Personalized Recommender System Using Entity Embeddings—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Understanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237—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
Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Universal Embeddings of Tabular Data—0
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification—0
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