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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 101–110 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
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