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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 51–75 of 151 papers

TitleStatusHype
Learning Relational Representations by Analogy using Hierarchical Siamese Networks—0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion—0
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation—0
A Joint Training Framework for Open-World Knowledge Graph Embeddings—0
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction—0
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning—0
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings—0
Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis—0
Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale—0
Global entity alignment with Gated Latent Space Neighborhood Aggregation—0
Knowledge Representation with Conceptual Spaces—0
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data—0
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion—0
DisenE: Disentangling Knowledge Graph Embeddings—0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks—0
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase—0
Improving Entity Linking by Modeling Latent Entity Type Information—0
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension—0
Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment—0
KECRS: Towards Knowledge-Enriched Conversational Recommendation System—0
Improving Question Answering over Knowledge Graphs Using Graph Summarization—0
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning—0
Improving Entity Linking by Encoding Type Information into Entity Embeddings—0
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