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Entity Embeddings

16 papers with code · Methodology

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Entity Embeddings of Categorical Variables

22 Apr 2016entron/entity-embedding-rossmann

As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering.

ENTITY EMBEDDINGS

DeepType: Multilingual Entity Linking by Neural Type System Evolution

3 Feb 2018openai/deeptype

The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.

ENTITY EMBEDDINGS ENTITY LINKING STOCHASTIC OPTIMIZATION

End-to-End Neural Entity Linking

CONLL 2018 dalab/end2end_neural_el

Entity Linking (EL) is an essential task for semantic text understanding and information extraction.

 SOTA for Entity Linking on AIDA-CoNLL (Micro-F1 metric )

ENTITY DISAMBIGUATION ENTITY EMBEDDINGS ENTITY LINKING

Jointly Learning Entity and Relation Representations for Entity Alignment

IJCNLP 2019 THU-KEG/Entity_Alignment_Papers

Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs).

ENTITY ALIGNMENT ENTITY EMBEDDINGS KNOWLEDGE GRAPHS

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

ICML 2017 INK-USC/RENet

The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.

ENTITY EMBEDDINGS KNOWLEDGE GRAPHS RELATIONAL REASONING

Multi-relational Poincaré Graph Embeddings

NeurIPS 2019 ibalazevic/multirelational-poincare

Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues.

ENTITY EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Multi-relational Poincaré Graph Embeddings

NeurIPS 2019 ibalazevic/multirelational-poincare

Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues.

ENTITY EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

ACL 2019 nju-websoft/JAPE

Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs.

ENTITY EMBEDDINGS GRAPH MATCHING