Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Berrendorf, Max"'
Autor:
Cochez, Michael, Alivanistos, Dimitrios, Arakelyan, Erik, Berrendorf, Max, Daza, Daniel, Galkin, Mikhail, Minervini, Pasquale, Niepert, Mathias, Ren, Hongyu
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and evolves, making existing facts deprecated or introducing new ones. Ho
Externí odkaz:
http://arxiv.org/abs/2308.06585
Autor:
Fromm, Michael, Berrendorf, Max, Reiml, Johanna, Mayerhofer, Isabelle, Bhargava, Siddharth, Faerman, Evgeniy, Seidl, Thomas
Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing attention. A decisive property of arguments is their strength or quality
Externí odkaz:
http://arxiv.org/abs/2205.09803
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics. Here, we review existing rank-based metrics and propose desiderata for improved metrics to address lack of
Externí odkaz:
http://arxiv.org/abs/2203.07544
An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a n
Externí odkaz:
http://arxiv.org/abs/2203.01520
Autor:
Gottfriedsen, Julia, Berrendorf, Max, Gentine, Pierre, Reichstein, Markus, Weigel, Katja, Hassler, Birgit, Eyring, Veronika
Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climat
Externí odkaz:
http://arxiv.org/abs/2111.15452
Autor:
Ali, Mehdi, Berrendorf, Max, Galkin, Mikhail, Thost, Veronika, Ma, Tengfei, Tresp, Volker, Lehmann, Jens
For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference
Externí odkaz:
http://arxiv.org/abs/2107.04894
Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. Existing algorithms operate only
Externí odkaz:
http://arxiv.org/abs/2106.08166
Autor:
Kronberg, Elena A., Hannan, Tanveer, Huthmacher, Jens, Münzer, Marcus, Peste, Florian, Zhou, Ziyang, Berrendorf, Max, Faerman, Evgeniy, Gastaldello, Fabio, Ghizzardi, Simona, Escoubet, Philippe, Haaland, Stein, Smirnov, Artem, Sivadas, Nithin, Allen, Robert C., Tiengo, Andrea, Ilie, Raluca
The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have derived machine learning-based models to predict the proton intensities at
Externí odkaz:
http://arxiv.org/abs/2105.15108
Autor:
Fromm, Michael, Faerman, Evgeniy, Berrendorf, Max, Bhargava, Siddharth, Qi, Ruoxia, Zhang, Yao, Dennert, Lukas, Selle, Sophia, Mao, Yang, Seidl, Thomas
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process and increasing interest in emerging fields often results in a high revi
Externí odkaz:
http://arxiv.org/abs/2012.07743
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit mappings between claims and premises, and thus are unable to utilize large available collecti
Externí odkaz:
http://arxiv.org/abs/2011.02177