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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 51–75 of 151 papers

TitleStatusHype
Entity-aware Transformers for Entity SearchCode1
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph CompletionCode0
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph CompletionCode0
Query2Particles: Knowledge Graph Reasoning with Particle EmbeddingsCode1
Improving Question Answering over Knowledge Graphs Using Graph Summarization—0
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings—0
Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion—0
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Rethinking Graph Convolutional Networks in Knowledge Graph CompletionCode1
Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment—0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion—0
Informed Multi-context Entity AlignmentCode0
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules—0
TempoQR: Temporal Question Reasoning over Knowledge GraphsCode1
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural NetworksCode1
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
SocialVec: Social Entity EmbeddingsCode0
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework—0
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification—0
Meta-Knowledge Transfer for Inductive Knowledge Graph EmbeddingCode1
Principled Representation Learning for Entity Alignment—0
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List ContinuationCode1
Complex Temporal Question Answering on Knowledge GraphsCode1
SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity EmbeddingsCode0
Enhancing Natural Language Representation with Large-Scale Out-of-Domain CommonsenseCode0
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