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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 81–90 of 151 papers

TitleStatusHype
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
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