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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 91–100 of 151 papers

TitleStatusHype
SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs—0
Supervised Typing of Big Graphs using Semantic Embeddings—0
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework—0
TorusE: Knowledge Graph Embedding on a Lie Group—0
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs—0
TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction—0
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment—0
Understanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237—0
Universal Embeddings of Tabular Data—0
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers—0
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