20 citations · 42 across the 6 of their papers we have counts for
8 papers
Weisfeiler and Leman Go Relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris +1
Knowledge graphs, modeling multi-relational data, improve numerous applications such as question answering or graph logical reasoning. Many graph neural networks for such data emer…
Inductive Logical Query Answering in Knowledge Graphs
Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren +1
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural metho…
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Phillip Schneider, Tim Schopf, Juraj Vladika +3
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a represen…
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…
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…