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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 61–70 of 151 papers

TitleStatusHype
Entity-Assisted Language Models for Identifying Check-worthy Sentences—0
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs—0
Inductive Logical Query Answering in Knowledge GraphsCode0
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
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
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