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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 131–140 of 151 papers

Title	Date	Tasks	Status
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities	Oct 10, 2024	Document RankingEntity Embeddings	CodeCode Available
SocialVec: Social Entity Embeddings	Nov 5, 2021	Entity EmbeddingsWord Embeddings	CodeCode Available
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network	May 28, 2019	Entity EmbeddingsGraph Attention	CodeCode Available
Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval	May 31, 2019	Entity EmbeddingsLanguage Modeling	CodeCode Available
Jointly Learning Entity and Relation Representations for Entity Alignment	Sep 20, 2019	Entity AlignmentEntity Embeddings	CodeCode Available
PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings	Jan 31, 2025	Entity EmbeddingsKnowledge Graph Embeddings	CodeCode Available
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs	Sep 20, 2022	Entity AlignmentEntity Embeddings	CodeCode Available
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation	Nov 13, 2019	Entity EmbeddingsEntity Typing	CodeCode Available
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs	May 16, 2017	Entity EmbeddingsKnowledge Graphs	CodeCode Available
An entity-guided text summarization framework with relational heterogeneous graph neural network	Feb 7, 2023	Entity EmbeddingsGraph Neural Network	CodeCode Available

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