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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 111–120 of 151 papers

TitleStatusHype
Neural Entity Linking: A Survey of Models Based on Deep Learning—0
Neural Relation Extraction for Knowledge Base Enrichment—0
Content-Based Personalized Recommender System Using Entity Embeddings—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Understanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237—0
Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph—0
“Politeness, you simpleton!” retorted [MASK]: Masked prediction of literary characters—0
Principled Representation Learning for Entity Alignment—0
Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
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