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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 1–10 of 151 papers

TitleStatusHype
Universal Embeddings of Tabular Data—0
Understanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237—0
SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment—0
PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph EmbeddingsCode0
LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs—0
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase—0
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with EntitiesCode0
DERA: Dense Entity Retrieval for Entity Alignment in Knowledge Graphs—0
RaTEScore: A Metric for Radiology Report GenerationCode4
Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification—0
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