9 citations · 34 across the 18 of their papers we have counts for
7 papers · 1 filter
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…
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
Jiaoyan Chen, Hang Dong, Janna Hastings +6
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.…
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…
Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning
Lucas Jarnac, Miguel Couceiro, Pierre Monnin
Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. S…
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…