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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 21–30 of 151 papers

Title	Date	Tasks	Status	Hype
Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering	Oct 6, 2022	Entity EmbeddingsGraph Neural Network	CodeCode Available	1
MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph	Feb 3, 2020	Entity AlignmentEntity Embeddings	CodeCode Available	1
CoLAKE: Contextualized Language and Knowledge Embedding	Oct 1, 2020	Entity EmbeddingsKnowledge Graph Completion	CodeCode Available	1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation	Mar 17, 2022	Entity EmbeddingsFederated Learning	CodeCode Available	1
Complex Temporal Question Answering on Knowledge Graphs	Sep 18, 2021	16kEntity Embeddings	CodeCode Available	1
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs	Aug 22, 2019	Entity AlignmentEntity Embeddings	CodeCode Available	1
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models	Jul 18, 2023	Entity AlignmentEntity Embeddings	CodeCode Available	1
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding	Feb 3, 2023	Entity EmbeddingsGraph Embedding	CodeCode Available	1
Contextual Parameter Generation for Knowledge Graph Link Prediction	Apr 3, 2020	Entity EmbeddingsLink Prediction	CodeCode Available	1
Inductive Learning on Commonsense Knowledge Graph Completion	Sep 19, 2020	DecoderEntity Embeddings	CodeCode Available	1

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