Entity Embeddings
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Greatest papers with code
Entity Embeddings of Categorical Variables
• entron/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.
DeepType: Multilingual Entity Linking by Neural Type System Evolution
The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
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.
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
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.
Jointly Learning Entity and Relation Representations for Entity Alignment
• THU-KEG/Entity_Alignment_Papers
• 
Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs).
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
ACL 2019
• THU-KEG/Entity_Alignment_Papers
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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.
Multi-relational Poincaré Graph Embeddings
• ibalazevic/multirelational-poincare
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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.
Merge and Label: A novel neural network architecture for nested NER
ACL 2019 • fishjh2/merge_label
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
#3 best model for
Nested Mention Recognition
on ACE 2005
ENTITY EMBEDDINGS NAMED ENTITY RECOGNITION NESTED MENTION RECOGNITION
Incorporating Literals into Knowledge Graph Embeddings
• SmartDataAnalytics/LiteralE
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Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.
ENTITY EMBEDDINGS KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION
Word Embeddings for Entity-annotated Texts
We discuss two distinct approaches to the generation of such embeddings, namely the training of state-of-the-art embeddings on raw text and annotated versions of the corpus, as well as node embeddings of a co-occurrence graph representation of the annotated corpus.