Knowledge Graph Embedding
196 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Knowledge Graph Embedding models and implementations
FB15k
KG20C
Latest papers
Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
xincanfeng/dimension
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COLING (TextGraphs) 2022
A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world.
Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding
dellixx/tkbc-lie
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Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task.
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts.
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>).
Do Similar Entities have Similar Embeddings?
nicolas-hbt/similar-embeddings
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A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i. e.}, position similar entities within the graph close to one another.
Universal Knowledge Graph Embeddings
Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting.
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
Graph data structures are widely used to store relational information between several entities.
Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction
Particularly in biomedical relationship prediction tasks, NC-KGE outperforms all baselines on datasets such as PharmKG8k-28, DRKG17k-21, and BioKG72k-14, especially in predicting drug combination relationships.
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
DeMix2023/Demix
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Most existing negative sampling methods assume that non-existent triples with high scores are high-quality negative triples.
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
