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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 61–70 of 151 papers

Title	Date	Tasks	Status	Hype	Score
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT	Nov 9, 2019	Entity EmbeddingsEntity Linking	CodeCode Available	0	5
Capturing Knowledge of Emerging Entities From Extended Search Snippets	Mar 17, 2021	Entity Embeddings	CodeCode Available	0	5
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation	Nov 13, 2019	Entity EmbeddingsEntity Typing	CodeCode Available	0	5
Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks	Dec 16, 2023	Entity EmbeddingsInductive Link Prediction	CodeCode Available	0	5
A Mixture-of-Experts Model for Learning Multi-Facet Entity Embeddings	Dec 1, 2020	Entity EmbeddingsMixture-of-Experts	CodeCode Available	0	5
Inductive Logical Query Answering in Knowledge Graphs	Oct 13, 2022	Complex Query AnsweringEntity Embeddings	CodeCode Available	0	5
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network	May 28, 2019	Entity EmbeddingsGraph Attention	CodeCode Available	0	5
Incorporating Literals into Knowledge Graph Embeddings	Feb 3, 2018	Entity EmbeddingsKnowledge Graph Embeddings	CodeCode Available	0	5
DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph	Sep 14, 2023	Entity EmbeddingsEntity Linking	CodeCode Available	0	5
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding	Jan 23, 2024	Entity AlignmentEntity Embeddings	CodeCode Available	0	5

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