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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 121–130 of 151 papers

TitleStatusHype
Neural Relation Extraction for Knowledge Base Enrichment—0
Merge and Label: A novel neural network architecture for nested NERCode0
Learning Relational Representations by Analogy using Hierarchical Siamese Networks—0
Abstract Graphs and Abstract Paths for Knowledge Graph Completion—0
Knowledge Hypergraphs: Prediction Beyond Binary RelationsCode0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural NetworkCode0
Multi-relational Poincaré Graph EmbeddingsCode0
RelWalk -- A Latent Variable Model Approach to Knowledge Graph Embedding—0
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning—0
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