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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 76–100 of 151 papers

TitleStatusHype
Word Embeddings for Entity-annotated TextsCode0
Apprendre des repr\'esentations jointes de mots et d'entit\'es pour la d\'esambigu\" d'entit\'es (Combining Word and Entity Embeddings for Entity Linking)—0
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models—0
Supervised Typing of Big Graphs using Semantic Embeddings—0
DisenE: Disentangling Knowledge Graph Embeddings—0
Interpretable Entity Representations through Large-Scale Typing—0
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning—0
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase—0
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers—0
Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment—0
KECRS: Towards Knowledge-Enriched Conversational Recommendation System—0
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension—0
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework—0
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules—0
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction—0
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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