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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 71–80 of 151 papers

TitleStatusHype
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
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
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
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
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