2 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
Do Similar Entities have Similar Embeddings?
Nicolas Hubert, Heiko Paulheim, Armelle Brun +1
Knowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for entities in a knowledge graph, known as embeddings. A common tacit assumptio…
PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
Nicolas Hubert, Pierre Monnin, Mathieu d'Aquin +2
Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only fact…
Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
Nicolas Hubert, Heiko Paulheim, Pierre Monnin +2
Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.…
Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction
Nicolas Hubert, Pierre Monnin, Armelle Brun +1
Knowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that consider…
Sem@: Is my knowledge graph embedding model semantic-aware?
Nicolas Hubert, Pierre Monnin, Armelle Brun +1
Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is…