27 citations · 27 across the 3 of their papers we have counts for
4 papers
MERIT: Scaling KG Foundation Models with Relation Transformers
Cosimo Gregucci, Daniel Hernandez, Antonio Vergari +1
Knowledge graph (KG) foundation models (KGFMs) predict links on unseen graphs zero-shot. KGFMs have been pioneered by ULTRA which builds a relation graph from co-occurrences that m…
Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models
Cosimo Gregucci, Obaidah Theeb, Daniel Hernandez +2
Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining. However, understanding when and…
Is Complex Query Answering Really Complex?
Cosimo Gregucci, Bo Xiong, Daniel Hernandez +4
Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA might not be…
Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models
Cosimo Gregucci, Mojtaba Nayyeri, Daniel Hernández +1
Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a…