Entity Embeddings
69 papers with code • 0 benchmarks • 2 datasets
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.
Benchmarks
These leaderboards are used to track progress in Entity Embeddings
DAWT
Most implemented papers
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.
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.
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
priyaradhakrishnan0/ELDEN
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NAACL 2018
Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG).
End-to-End Neural Entity Linking
Entity Linking (EL) is an essential task for semantic text understanding and information extraction.
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.
Multi-relational Poincaré Graph Embeddings
ibalazevic/multirelational-poincare
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NeurIPS 2019
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.
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
syxu828/Crosslingula-KG-Matching
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ACL 2019
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.
Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval
Tables contain valuable knowledge in a structured form.
Knowledge Hypergraphs: Prediction Beyond Binary Relations
ElementAI/HypE
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Knowledge graphs store facts using relations between two entities.
Merge and Label: A novel neural network architecture for nested NER
fishjh2/merge_label
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ACL 2019
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
