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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

Showing 125 of 151 papers

TitleStatusHype
RaTEScore: A Metric for Radiology Report GenerationCode4
OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest SearchCode2
Scalable Zero-shot Entity Linking with Dense Entity RetrievalCode2
Rethinking Graph Convolutional Networks in Knowledge Graph CompletionCode1
CoLAKE: Contextualized Language and Knowledge EmbeddingCode1
Relation-Aware Entity Alignment for Heterogeneous Knowledge GraphsCode1
Message Passing Query EmbeddingCode1
Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question AnsweringCode1
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List ContinuationCode1
InGram: Inductive Knowledge Graph Embedding via Relation GraphsCode1
MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge GraphCode1
Query2Particles: Knowledge Graph Reasoning with Particle EmbeddingsCode1
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language ModelsCode1
RDF2Vec: RDF Graph Embeddings and Their ApplicationsCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge GraphsCode1
Complex Temporal Question Answering on Knowledge GraphsCode1
Entity-aware Transformers for Entity SearchCode1
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch SimilaritiesCode1
Contextual Parameter Generation for Knowledge Graph Link PredictionCode1
Improving Entity Linking through Semantic Reinforced Entity EmbeddingsCode1
Inductive Learning on Commonsense Knowledge Graph CompletionCode1
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph EmbeddingCode1
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes AnalysisCode1
MMEAD: MS MARCO Entity Annotations and DisambiguationsCode1
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