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pro vyhledávání: '"Monnin, A."'
Autor:
Monnin, Pierre, Nousradine, Cherif-Hassan, Jarnac, Lucas, Zuckerman, Laurel, Couceiro, Miguel
Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains. However, not all knowledge represented is useful or pertaining when considering a new application or sp
Externí odkaz:
http://arxiv.org/abs/2408.14658
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 suffer from incompleteness, i.e. entities or facts about entities are mi
Externí odkaz:
http://arxiv.org/abs/2312.04997
Autor:
Chen, Jiaoyan, Dong, Hang, Hastings, Janna, Jiménez-Ruiz, Ernesto, López, Vanessa, Monnin, Pierre, Pesquita, Catia, Škoda, Petr, Tamma, Valentina
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. Research efforts in life sciences are heavily data-driven, as they pr
Externí odkaz:
http://arxiv.org/abs/2309.17255
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 factual information but also contextual knowledge. In some tasks, a few KG
Externí odkaz:
http://arxiv.org/abs/2309.03685
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. Such a nucleus can be obtained from knowledge existing in an open KG li
Externí odkaz:
http://arxiv.org/abs/2306.16296
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.k.a. knowledge graph embeddings (KGEs). Learning versatile KGEs is des
Externí odkaz:
http://arxiv.org/abs/2306.03659
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 batches of true and false triples. However, different kinds of false
Externí odkaz:
http://arxiv.org/abs/2303.00286
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 assessed using rank-based metrics, which evaluate their ability to giv
Externí odkaz:
http://arxiv.org/abs/2301.05601
Autor:
Justin Fuhr, Caroline Monnin
Publikováno v:
Quantitative Science Studies, Vol 5, Iss 3 (2024)
Externí odkaz:
https://doaj.org/article/87dd08a8ce91481fb07ac0303e2da452
Autor:
Monnin, Dominique, Neto Silva, Ivo
Publikováno v:
In Kinésithérapie, la revue December 2024 24(276):25-27