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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 31–40 of 151 papers

TitleStatusHype
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion—0
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge GraphsCode1
InGram: Inductive Knowledge Graph Embedding via Relation GraphsCode1
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference ChainsCode0
An entity-guided text summarization framework with relational heterogeneous graph neural networkCode0
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph EmbeddingCode1
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension—0
Entity-Assisted Language Models for Identifying Check-worthy Sentences—0
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs—0
Inductive Logical Query Answering in Knowledge GraphsCode0
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