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With the recent surge in popularity of Large Language Models (LLMs), there is the rising risk of users blindly trusting the information in the response, even in cases where the LLM recommends actions that have potential legal implications and this ma
Externí odkaz:
http://arxiv.org/abs/2410.15064
Autor:
Sousa, Rita T., Paulheim, Heiko
Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gen
Externí odkaz:
http://arxiv.org/abs/2404.14970
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
Publikováno v:
In Tunnelling and Underground Space Technology incorporating Trenchless Technology Research November 2024 153
Autor:
de Sousa, Renato Pinto, de Oliveira, Carlos Mario Freitas, de Lima Sousa, Rita de Cassia, Leite, Lucas Luan Lima, Oliveira, André Luiz Ozório, Ferreira, Jady Vitoria Barjud Pereira, de Freitas Pessoa, Ana Marcia, Oliveira, Jorge Luiz Silva, Meireles, Victor de Jesus Silva, Lustosa Barros, Esmeralda Maria, Viana Nunes, Adriana Maria, Chaves, Mariana Helena, do Amaral, Maurício Pires de Moura, Vieira Júnior, Gerardo Magela
Publikováno v:
In Toxicon October 2024 249
Publikováno v:
In Tunnelling and Underground Space Technology incorporating Trenchless Technology Research April 2024 146
Publikováno v:
In Computers in Biology and Medicine March 2024 170