129 citations · 314 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…