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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
Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question AnsweringCode1
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge GraphsCode0
Embedding-Based Entity Alignment Using Relation Structural SimilarityCode0
Conflict-Aware Pseudo Labeling via Optimal Transport for Entity AlignmentCode0
Ered: Enhanced Text Representations with Entities and DescriptionsCode0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph CompletionCode0
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop SubgraphCode1
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch SimilaritiesCode1
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