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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 71–80 of 151 papers

TitleStatusHype
Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion—0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion—0
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data—0
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network—0
LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs—0
Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings—0
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models—0
Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis—0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities—0
Neural Entity Linking: A Survey of Models Based on Deep Learning—0
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