9 citations · 34 across the 18 of their papers we have counts for
6 papers · 1 filter
Oracle, will I ever learn? A study of prediction convergence and complementarity across link prediction models
Guillaume Méroué, Fabien Gandon, Pierre Monnin
Knowledge graphs have become an important source of structured knowledge for Web applications, including search, question answering, and recommender systems. In these applications,…
Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
Guillaume Méroué, Fabien Gandon, Pierre Monnin
Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs. Standard evaluation protocols emphasize rank-based metrics such as MRR or Hits@$…
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…
Investigating ADR mechanisms with knowledge graph mining and explainable AI
Emmanuel Bresso, Pierre Monnin, Cédric Bousquet +5
Adverse Drug Reactions (ADRs) are characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases.…
Discovering alignment relations with Graph Convolutional Networks: a biomedical case study
Pierre Monnin, Chedy Raïssi, Amedeo Napoli +1
Knowledge graphs are freely aggregated, published, and edited in the Web of data, and thus may overlap. Hence, a key task resides in aligning (or matching) their content. This task…