no code implementations • 12 Aug 2023 • Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
no code implementations • 19 May 2022 • Michael Fromm, Max Berrendorf, Johanna Reiml, Isabelle Mayerhofer, Siddharth Bhargava, Evgeniy Faerman, Thomas Seidl
While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal.
2 code implementations • 14 Mar 2022 • Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin, Volker Tresp, Benjamin M. Gyori
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
1 code implementation • 3 Mar 2022 • Mikhail Galkin, Max Berrendorf, Charles Tapley Hoyt
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 new graph with unseen entities.
Ranked #1 on Inductive Link Prediction on ILPC22-Small
1 code implementation • 30 Nov 2021 • Julia Gottfriedsen, Max Berrendorf, Pierre Gentine, Markus Reichstein, Katja Weigel, Birgit Hassler, Veronika Eyring
Climate change is expected to increase the likelihood of drought events, with severe implications for food security.
2 code implementations • 10 Jul 2021 • Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
1 code implementation • ICLR 2022 • Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, Mikhail Galkin
Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
1 code implementation • 11 May 2021 • Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher, Marcus Münzer, Florian Peste, Ziyang Zhou, Max Berrendorf, Evgeniy Faerman, Fabio Gastaldello, Simona Ghizzardi, Philippe Escoubet, Stein Haaland, Artem Smirnov, Nithin Sivadas, Robert C. Allen, Andrea Tiengo, Raluca Ilie
The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere.
2 code implementations • 10 Dec 2020 • Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.
1 code implementation • 4 Nov 2020 • Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.
1 code implementation • 30 Oct 2020 • Max Berrendorf, Ludwig Wacker, Evgeniy Faerman
Therefore, we first carefully examine the benchmarking process and identify several shortcomings, which make the results reported in the original works not always comparable.
Ranked #11 on Entity Alignment on dbp15k fr-en
2 code implementations • 28 Jul 2020 • Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on Link Prediction on WN18 (training time (s) metric)
2 code implementations • 23 Jun 2020 • Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
1 code implementation • 17 Feb 2020 • Max Berrendorf, Evgeniy Faerman, Laurent Vermue, Volker Tresp
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment.
1 code implementation • 29 Jan 2020 • Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max Berrendorf, Evgeniy Faerman, Michael Fromm, Matthias Schubert
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
1 code implementation • 24 Jan 2020 • Max Berrendorf, Evgeniy Faerman, Volker Tresp
In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets.
1 code implementation • 19 Nov 2019 • Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl
In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.
Ranked #33 on Entity Alignment on DBP15k zh-en
1 code implementation • 2 May 2019 • Sahand Sharifzadeh, Sina Moayed Baharlou, Max Berrendorf, Rajat Koner, Volker Tresp
We argue that depth maps can additionally provide valuable information on object relations, e. g. helping to detect not only spatial relations, such as standing behind, but also non-spatial relations, such as holding.
Ranked #1 on Relationship Detection on VRD