4 citations · 6 across the 8 of their papers we have counts for
10 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…
Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation
Konrad Özdemir, Julia Gastinger, Lukas Kirchdorfer +1
Temporal knowledge graphs (TKGs) represent evolving relational systems, whose underlying data-generating processes often change over time. Yet, TKG forecasting models are commonly…
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…
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger, Shenyang Huang, Mikhail Galkin +9
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel model…
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…
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger +7
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes rese…
ProcK: Machine Learning for Knowledge-Intensive Processes
Tobias Jacobs, Jingyi Yu, Julia Gastinger +1
We present a novel methodology to build powerful predictive process models. Our method, denoted ProcK (Process & Knowledge), relies not only on sequential input data in the form of…
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning
Julia Gastinger, Sébastien Nicolas, Dušica Stepić +2
The contribution of this work is twofold: (1) We introduce a collection of ensemble methods for time series forecasting to combine predictions from base models. We demonstrate insi…
HAMLET -- A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection
Mischa Schmidt, Julia Gastinger, Sébastien Nicolas +1
Automated algorithm selection and hyperparameter tuning facilitates the application of machine learning. Traditional multi-armed bandit strategies look to the history of observed r…
On the Performance of Differential Evolution for Hyperparameter Tuning
Mischa Schmidt, Shahd Safarani, Julia Gastinger +3
Automated hyperparameter tuning aspires to facilitate the application of machine learning for non-experts. In the literature, different optimization approaches are applied for that…