Knowledge Graph Embedding
199 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Knowledge Graph Embedding models and implementations
FB15k
KG20C
Latest papers with no code
Open Knowledge Base Canonicalization with Multi-task Unlearning
MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training.
Knowledge Graph Embedding: An Overview
We will also discuss an emerging approach for KG completion which leverages pre-trained language models (PLMs) and textual descriptions of entities and relations and offer insights into the integration of KGE embedding methods with PLMs for KG completion.
RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?
We initialize the weights of the entities with these embeddings to train our knowledge graph embedding (KGE) model.
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML).
Fast Knowledge Graph Completion using Graphics Processing Units
After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space.
Contextual Dictionary Lookup for Knowledge Graph Completion
We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.
Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model
To overcome these challenges, we propose to infer the conditional dependency structure among EHR features via a latent graphical block model (LGBM).
Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development.
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks.
Joint embedding in Hierarchical distance and semantic representation learning for link prediction
Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space.
