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 51–100 of 151 papers

TitleStatusHype
Embedding Knowledge Graphs in Degenerate Clifford Algebras—0
Entity-Assisted Language Models for Identifying Check-worthy Sentences—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
Entity Embedding as Game Representation—0
Graph Neural Pre-training for Enhancing Recommendations using Side Information—0
Graph Reasoning for Explainable Cold Start Recommendation—0
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users—0
Improving Entity Linking by Encoding Type Information into Entity Embeddings—0
Improving Entity Linking by Modeling Latent Entity Type Information—0
Improving Question Answering over Knowledge Graphs Using Graph Summarization—0
Interpretable Entity Representations through Large-Scale Typing—0
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning—0
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase—0
Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment—0
KECRS: Towards Knowledge-Enriched Conversational Recommendation System—0
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules—0
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction—0
Knowledge Representation with Conceptual Spaces—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Learning Relational Representations by Analogy using Hierarchical Siamese Networks—0
Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion—0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion—0
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data—0
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network—0
LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs—0
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
Show:102550
← PrevPage 2 of 4Next →

No leaderboard results yet.