Entity Embeddings
50 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Entity Embeddings
DAWT
Most implemented papers
Entity Embeddings of Categorical Variables
entron/entity-embedding-rossmann
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As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering.
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
NeverInAsh/fit-recommendation
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To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
facebookresearch/BLINK
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EMNLP 2020
This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off.
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.
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.
DAWT: Densely Annotated Wikipedia Texts across multiple languages
In addition to the main dataset, we open up several derived datasets including mention entity co-occurrence counts and entity embeddings, as well as mappings between Freebase ids and Wikidata item ids.
Named Entity Disambiguation for Noisy Text
yotam-happy/NEDforNoisyText
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CONLL 2017
We address the task of Named Entity Disambiguation (NED) for noisy text.
RDF2Vec: RDF Graph Embeddings and Their Applications
Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks.
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.
