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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
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion—0
Informed Multi-context Entity AlignmentCode0
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules—0
TempoQR: Temporal Question Reasoning over Knowledge GraphsCode1
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural NetworksCode1
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
SocialVec: Social Entity EmbeddingsCode0
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework—0
RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification—0
Meta-Knowledge Transfer for Inductive Knowledge Graph EmbeddingCode1
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