31 citations · 36 across the 7 of their papers we have counts for
8 papers
CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting
Julia Gastinger, Christian Meilicke, Heiner Stuckenschmidt
We address the task of temporal knowledge graph forecasting with an inherently interpretable method based on symbolic rules. Motivated by recent work proposing a strong baseline ba…
A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
Patrick Betz, Nathanael Stelzner, Christian Meilicke +2
In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph…
Reevaluation of Inductive Link Prediction
Simon Ott, Christian Meilicke, Heiner Stuckenschmidt
Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of ran…
History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
Julia Gastinger, Christian Meilicke, Federico Errica +3
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 evalu…
On the Aggregation of Rules for Knowledge Graph Completion
Patrick Betz, Stefan Lüdtke, Christian Meilicke +1
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding…
Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs
Simon Ott, Laura Graf, Asan Agibetov +2
Neural embedding-based machine learning models have shown promise for predicting novel links in biomedical knowledge graphs. Unfortunately, their practical utility is diminished by…