Search Results for author: Max Berrendorf

Found 14 papers, 13 papers with code

On the Generalization of Agricultural Drought Classification from Climate Data

no code implementations30 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.

Improving Inductive Link Prediction Using Hyper-Relational Facts

1 code implementation10 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.

Knowledge Graphs Link Prediction

Query Embedding on Hyper-relational Knowledge Graphs

1 code implementation15 Jun 2021 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.

Knowledge Graphs Link Prediction +1

Prediction of soft proton intensities in the near-Earth space using machine learning

1 code implementation11 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.

Argument Mining Driven Analysis of Peer-Reviews

1 code implementation10 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.

Argument Mining

Diversity Aware Relevance Learning for Argument Search

1 code implementation4 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.

Argument Retrieval

A Critical Assessment of State-of-the-Art in Entity Alignment

1 code implementation30 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.

Entity Alignment Hyperparameter Optimization +1

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

2 code implementations28 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)

Knowledge Graph Embedding Knowledge Graph Embeddings +1

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

2 code implementations23 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.

Knowledge Graph Embedding

On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods

1 code implementation17 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.

Entity Alignment Informativeness +2

Unsupervised Anomaly Detection for X-Ray Images

1 code implementation29 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.

Unsupervised Anomaly Detection

Active Learning for Entity Alignment

1 code implementation24 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.

Active Learning Entity Alignment

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

1 code implementation19 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.

Entity Alignment Graph Convolutional Network +1

Improving Visual Relation Detection using Depth Maps

1 code implementation2 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.

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