Christian Meilicke — accepted AI-conference papers

2024

History Repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting

Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schülke, Heiner Stuckenschmidt

IJCAI 2024poster

Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are available, but the importance of simple baselines is often…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2024

PyClause - Simple and Efficient Rule Handling for Knowledge Graphs

Patrick Betz, Luis Galárraga, Simon Ott, Christian Meilicke, Fabian Suchanek, Heiner Stuckenschmidt

IJCAI 2024poster

Rule mining finds patterns in structured data such as knowledge graphs. Rules can predict facts, help correct errors, and yield explainable insights about the data. However, existing rule mining implementations focus exclusively on mining rules -- and not on their application. The PyClause library o…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2022

Adversarial Explanations for Knowledge Graph Embeddings

Patrick Betz, Christian Meilicke, Heiner Stuckenschmidt

IJCAI 2022poster

We propose a novel black-box approach for performing adversarial attacks against knowledge graph embedding models. An adversarial attack is a small perturbation of the data at training time to cause model failure at test time. We make use of an efficient rule learning approach and use abductive reas…

Cited by 34 Source ↗PDF ↗Scholar ↗