3 citations · 9 across the 6 of their papers we have counts for
6 papers
Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs
Nicolas Hubert, Pierre Monnin, Heiko Paulheim
Knowledge graphs (KGs) comprise entities interconnected by relations of different semantic meanings. KGs are being used in a wide range of applications. However, they inherently su…
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…