129 citations · 314 across the 16 of their papers we have counts for
20 papers
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
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…
Towards a Holistic View on Argument Quality Prediction
Michael Fromm, Max Berrendorf, Johanna Reiml +4
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 at…
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin +2
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 m…
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
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 ta…
On the Generalization of Agricultural Drought Classification from Climate Data
Julia Gottfriedsen, Max Berrendorf, Pierre Gentine +4
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…
Improving Inductive Link Prediction Using Hyper-Relational Facts
Mehdi Ali, Max Berrendorf, Mikhail Galkin +4
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 i…
Query Embedding on Hyper-relational Knowledge Graphs
Dimitrios Alivanistos, Max Berrendorf, Michael Cochez +1
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 m…
Prediction of soft proton intensities in the near-Earth space using machine learning
Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher +14
The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have deriv…
Argument Mining Driven Analysis of Peer-Reviews
Michael Fromm, Evgeniy Faerman, Max Berrendorf +7
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 a…
Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation
Sandra Obermeier, Max Berrendorf, Peer Kröger
The reverse k-nearest neighbor (RkNN) query is an established query type with various applications reaching from identifying highly influential objects over incrementally updating…
Diversity Aware Relevance Learning for Argument Search
Michael Fromm, Max Berrendorf, Sandra Obermeier +2
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…
A Critical Assessment of State-of-the-Art in Entity Alignment
Max Berrendorf, Ludwig Wacker, Evgeniy Faerman
In this work, we perform an extensive investigation of two state-of-the-art (SotA) methods for the task of Entity Alignment in Knowledge Graphs. Therefore, we first carefully exami…
Prediction and understanding of soft proton contamination in XMM-Newton: a machine learning approach
E. A. Kronberg, F. Gastaldello, S. Haaland +10
One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes are few tens to hundreds of keV (soft) protons concentrated by th…
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +4
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them…
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt +6
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult. In order to as…
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
Max Berrendorf, Evgeniy Faerman, Laurent Vermue +1
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. In the curre…
Unsupervised Anomaly Detection for X-Ray Images
Diana Davletshina, Valentyn Melnychuk, Viet Tran +5
Obtaining labels for medical (image) data requires scarce and expensive experts. Moreover, due to ambiguous symptoms, single images rarely suffice to correctly diagnose a medical c…
Active Learning for Entity Alignment
Max Berrendorf, Evgeniy Faerman, Volker Tresp
In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets. Different strategies to select informative instances for the human lab…
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk +2
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 fo…
Improving Visual Relation Detection using Depth Maps
Sahand Sharifzadeh, Sina Moayed Baharlou, Max Berrendorf +2
Visual relation detection methods rely on object information extracted from RGB images such as 2D bounding boxes, feature maps, and predicted class probabilities. We argue that dep…