Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Pesquita Catia"'
Knowledge Graphs (KG) are the backbone of many data-intensive applications since they can represent data coupled with its meaning and context. Aligning KGs across different domains and providers is necessary to afford a fuller and integrated represen
Externí odkaz:
http://arxiv.org/abs/2310.07417
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
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, especially under
Externí odkaz:
http://arxiv.org/abs/2308.03447
Publikováno v:
International Conference on Principles of Knowledge Representation and Reasoning 2023
Knowledge graphs represent facts about real-world entities. Most of these facts are defined as positive statements. The negative statements are scarce but highly relevant under the open-world assumption. Furthermore, they have been demonstrated to im
Externí odkaz:
http://arxiv.org/abs/2307.11719
Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent representa
Externí odkaz:
http://arxiv.org/abs/2306.12687
Autor:
Falcão André O, Ferreira António EN, Bastos Hugo, Faria Daniel, Pesquita Catia, Couto Francisco M
Publikováno v:
BMC Bioinformatics, Vol 9, Iss Suppl 5, p S4 (2008)
Abstract Background Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic sim
Externí odkaz:
https://doaj.org/article/baad1e199dd741d082ea95ffb818d15e
Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semantic similarity is typically restricted to hierarchical relations within
Externí odkaz:
http://arxiv.org/abs/2105.04944
Publikováno v:
In Computers in Biology and Medicine March 2024 170