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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
Interpretable Entity Representations through Large-Scale Typing—0
TransEdge: Translating Relation-contextualized Embeddings for Knowledge GraphsCode1
Contextual Parameter Generation for Knowledge Graph Link PredictionCode1
Message Passing Query EmbeddingCode1
MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge GraphCode1
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network—0
Improving Entity Linking by Modeling Latent Entity Type Information—0
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction—0
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
Scalable Zero-shot Entity Linking with Dense Entity RetrievalCode2
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERTCode0
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models—0
KRED: Knowledge-Aware Document Representation for News RecommendationsCode0
Aligning Cross-Lingual Entities with Multi-Aspect InformationCode0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
Jointly Learning Entity and Relation Representations for Entity AlignmentCode0
Relation-Aware Entity Alignment for Heterogeneous Knowledge GraphsCode1
A Deep Learning System for Predicting Size and Fit in Fashion E-CommerceCode0
Neural Relation Extraction for Knowledge Base Enrichment—0
Merge and Label: A novel neural network architecture for nested NERCode0
Learning Relational Representations by Analogy using Hierarchical Siamese Networks—0
Abstract Graphs and Abstract Paths for Knowledge Graph Completion—0
Knowledge Hypergraphs: Prediction Beyond Binary RelationsCode0
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