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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
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
Improving Entity Linking by Encoding Type Information into Entity Embeddings—0
Entity Embedding as Game Representation—0
A Joint Training Framework for Open-World Knowledge Graph Embeddings—0
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning—0
Content-Based Personalized Recommender System Using Entity Embeddings—0
KECRS: Towards Knowledge-Enriched Conversational Recommendation System—0
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
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