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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 101–150 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
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural NetworkCode0
Multi-relational Poincaré Graph EmbeddingsCode0
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding—0
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning—0
Word Embeddings for Entity-annotated TextsCode0
End-to-End Neural Entity LinkingCode0
Knowledge Representation with Conceptual Spaces—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
ELDEN: Improved Entity Linking Using Densified Knowledge GraphsCode0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
DeepType: Multilingual Entity Linking by Neural Type System EvolutionCode0
Incorporating Literals into Knowledge Graph EmbeddingsCode0
TorusE: Knowledge Graph Embedding on a Lie Group—0
RDF2Vec: RDF Graph Embeddings and Their ApplicationsCode1
Convolutional Neural Knowledge Graph Learning—0
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings—0
Named Entity Disambiguation for Noisy TextCode0
Apprendre des repr\'esentations jointes de mots et d'entit\'es pour la d\'esambigu\" d'entit\'es (Combining Word and Entity Embeddings for Entity Linking)—0
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsCode0
Supervised Typing of Big Graphs using Semantic Embeddings—0
DAWT: Densely Annotated Wikipedia Texts across multiple languages—0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities—0
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data—0
Entity Embeddings of Categorical VariablesCode0
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