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
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.
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation
The personalized list continuation (PLC) task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way.
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief.
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate.
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood.
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.
