most citedBeyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs

3 citations · 9 across the 6 of their papers we have counts for

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cs.LG2023★ 1 cited

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…

cs.LG2023★ 2 cited

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…