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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 41–50 of 151 papers

TitleStatusHype
Graph Reasoning for Explainable Cold Start Recommendation—0
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users—0
Unlocking the Power of Large Language Models for Entity AlignmentCode0
Embedding Knowledge Graphs in Degenerate Clifford Algebras—0
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment—0
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment DecodingCode0
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation ExtractionCode0
Inductive Link Prediction in Knowledge Graphs using Path-based Neural NetworksCode0
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion—0
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
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