Search Results for author:
Found 6 papers, 6 papers with code
A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information.
Ranked #1 on
Inductive Link Prediction
on Wikidata5M-SI
Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction
We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG).
Ranked #1 on
Link Prediction
on Wikidata5M
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data.
Ranked #11 on
Link Prediction
on YAGO3-10
(MRR metric)
Sequence-to-Sequence Knowledge Graph Completion and Question Answering
These methods have recently been applied to KG link prediction and question answering over incomplete KGs (KGQA).
Ranked #5 on
Link Prediction
on Wikidata5M
Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques
We found that the evaluation methodologies used in prior work are often not comparable and can be misleading, and that most of currently implemented training methods tend to have a negative impact on embedding quality.
Ranked #6 on
Link Prediction
on Wikidata5M
LibKGE - A knowledge graph embedding library for reproducible research
LibKGE ( https://github. com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction.
