3 citations · 8 across the 10 of their papers we have counts for
6 papers · 1 filter
Gaining a better understanding of online polarization by approaching it as a dynamic process
Celina Treuillier, Sylvain Castagnos, Christèle Lagier +1
Polarization is often a clich{é}, its conceptualization remains approximate and no consensus has been reached so far. Often simply seen as an inevitable result of the use of social…
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.…
A Multi-Factorial Analysis of Polarization on Social Media
Celina Treuillier, Sylvain Castagnos, Armelle Brun
Polarization is an increasingly worrying phenomenon within social media. Recent work has made it possible to detect and even quantify polarization. Nevertheless, the few existing m…
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…
Characterizing Financial Market Coverage using Artificial Intelligence
Jean Marie Tshimula, D'Jeff K. Nkashama, Patrick Owusu +7
This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage. Financial market coverage generates a large number of videos. Therefore, wa…
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…