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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 91–100 of 151 papers

TitleStatusHype
Knowledge Representation with Conceptual Spaces—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Learning Relational Representations by Analogy using Hierarchical Siamese Networks—0
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
TorusE: Knowledge Graph Embedding on a Lie Group—0
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs—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
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