31 citations · 44 across the 10 of their papers we have counts for
11 papers
A Temporal Knowledge Graph for Music Festival Lineup Forecasting
Julia Gastinger, Thilo Dieing, Christian Meilicke +1
Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future lineups a natural fit for t…
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…
SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models
Simon Ott, Christian Meilicke, Matthias Samwald
Neural embedding-based machine learning models have shown promise for predicting novel links in knowledge graphs. Unfortunately, their practical utility is diminished by their lack…
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…
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion
Christian Meilicke, Melisachew Wudage Chekol, Manuel Fink +1
Most of todays work on knowledge graph completion is concerned with sub-symbolic approaches that focus on the concept of embedding a given graph in a low dimensional vector space.…
Using Abduction in Markov Logic Networks for Root Cause Analysis
Joerg Schoenfisch, Janno von Stulpnagel, Jens Ortmann +2
IT infrastructure is a crucial part in most of today's business operations. High availability and reliability, and short response times to outages are essential. Thus a high amount…
Evaluating Ontology Matching Systems on Large, Multilingual and Real-world Test Cases
Christian Meilicke, Ondrej Sváb-Zamazal, Cássia Trojahn +4
In the field of ontology matching, the most systematic evaluation of matching systems is established by the Ontology Alignment Evaluation Initiative (OAEI), which is an annual camp…