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pro vyhledávání: '"Monnin, Pierre"'
Autor:
Chen, Jiaoyan, Dong, Hang, Hastings, Janna, Jiménez-Ruiz, Ernesto, López, Vanessa, Monnin, Pierre, Pesquita, Catia, Škoda, Petr, Tamma, Valentina
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 5:1-5:33 (2023)
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:
https://doaj.org/article/4ff40f5f488842c7a6167ec1eaade3d0
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 8:1-8:35 (2023)
The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. H
Externí odkaz:
https://doaj.org/article/e696bd6a9ef64d9e89232b45b4077f3c
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
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:
Bresso, Emmanuel, Monnin, Pierre, Bousquet, Cédric, Calvier, François-Elie, Ndiaye, Ndeye-Coumba, Petitpain, Nadine, Smaïl-Tabbone, Malika, Coulet, Adrien
Adverse Drug Reactions (ADRs) are characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. Aside from clinical trials, many elements of knowledge about drug ingr
Externí odkaz:
http://arxiv.org/abs/2012.09077
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 encompasses the identification, within an aggregated knowledge graph,
Externí odkaz:
http://arxiv.org/abs/2011.06023