Zobrazeno 1 - 10
of 378
pro vyhledávání: '"Paliouras, Georgios"'
In this work we investigate an observation made by Kipf \& Welling, who suggested that untrained GCNs can generate meaningful node embeddings. In particular, we investigate the effect of training only a single layer of a GCN, while keeping the rest o
Externí odkaz:
http://arxiv.org/abs/2410.13416
We present a system for Complex Event Recognition (CER) based on automata. While multiple such systems have been described in the literature, they typically suffer from a lack of clear and denotational semantics, a limitation which often leads to con
Externí odkaz:
http://arxiv.org/abs/2407.02884
Autor:
Nentidis, Anastasios, Katsimpras, Georgios, Krithara, Anastasia, López, Salvador Lima, Farré-Maduell, Eulália, Gasco, Luis, Krallinger, Martin, Paliouras, Georgios
Publikováno v:
CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham
This is an overview of the eleventh edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ is a series of international challenges promoting advances in large-scale biomedical semantic in
Externí odkaz:
http://arxiv.org/abs/2307.05131
Autor:
Zavitsanos, Elias, Mavroeidis, Dimitris, Bougiatiotis, Konstantinos, Spyropoulou, Eirini, Loukas, Lefteris, Paliouras, Georgios
Publikováno v:
Proceedings of the Second ACM International Conference on AI in Finance, no 34, 2021
In this work, we examine the evaluation process for the task of detecting financial reports with a high risk of containing a misstatement. This task is often referred to, in the literature, as ``misstatement detection in financial reports''. We provi
Externí odkaz:
http://arxiv.org/abs/2305.17457
Most phenomena related to biomedical tasks are inherently complex, and in many cases, are expressed as signals on biomedical Knowledge Graphs (KGs). In this work, we introduce the use of a new representation framework, the Prime Adjacency Matrix (PAM
Externí odkaz:
http://arxiv.org/abs/2305.10467
Autor:
Nentidis, Anastasios, Chatzopoulos, Thomas, Krithara, Anastasia, Tsoumakas, Grigorios, Paliouras, Georgios
Publikováno v:
Journal of Biomedical Informatics, Volume 146, 2023, 104499, ISSN 1532-0464
Objective: Semantic indexing of biomedical literature is usually done at the level of MeSH descriptors with several related but distinct biomedical concepts often grouped together and treated as a single topic. This study proposes a new method for th
Externí odkaz:
http://arxiv.org/abs/2301.09350
Autor:
Nentidis, Anastasios, Katsimpras, Georgios, Vandorou, Eirini, Krithara, Anastasia, Miranda-Escalada, Antonio, Gasco, Luis, Krallinger, Martin, Paliouras, Georgios
Publikováno v:
Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2022. Lecture Notes in Computer Science, vol 13390. Springer, Cham
This paper presents an overview of the tenth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2022. BioASQ is an ongoing series of challenges that promotes advances in the domain of large-scale
Externí odkaz:
http://arxiv.org/abs/2210.06852
In this work, we propose a novel representation of complex multi-relational networks, which is compact and allows very efficient network analysis. Multi-relational networks capture complex data relationships and have a variety of applications, rangin
Externí odkaz:
http://arxiv.org/abs/2209.06575
Autor:
Katzouris, Nikos, Paliouras, Georgios
Complex Event Recognition and Forecasting (CER/F) techniques attempt to detect, or even forecast ahead of time, event occurrences in streaming input using predefined event patterns. Such patterns are not always known in advance, or they frequently ch
Externí odkaz:
http://arxiv.org/abs/2208.14820
Autor:
Sakor, Ahmad, Jozashoori, Samaneh, Niazmand, Emetis, Rivas, Ariam, Bougiatiotis, Kostantinos, Aisopos, Fotis, Iglesias, Enrique, Rohde, Philipp D., Padiya, Trupti, Krithara, Anastasia, Paliouras, Georgios, Vidal, Maria-Esther
In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug-drug interactions among COVID-19 treatments and pre-existing condi
Externí odkaz:
http://arxiv.org/abs/2206.07375