Entity Embeddings
70 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
Latest papers with no code
Social World Knowledge: Modeling and Applications
In this work, we elicited the social embeddings of roughly 200K entities from a sample of 1. 3M Twitter users and the accounts that they follow.
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples.
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension
Drug-drug interaction prediction is a crucial issue in molecular biology.
Entity-Assisted Language Models for Identifying Check-worthy Sentences
Our results show that the neural language models significantly outperform traditional TF. IDF and LSTM methods.
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity.
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph.
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation
The smoothing is specially desired in the presence of homophilic graphs, such as the ones we find on recommender systems.
Improving Question Answering over Knowledge Graphs Using Graph Summarization
The key idea is to represent questions and entities of a KG as low-dimensional embeddings.
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings
Semantic matching models -- which assume that entities with similar semantics have similar embeddings -- have shown great power in knowledge graph embeddings (KGE).
Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion
To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs.
