SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–110 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
Show:102550
← PrevPage 11 of 16Next →

No leaderboard results yet.