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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 51–60 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
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