TKG Forecasting: Recurrency_Baselines

"History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting" Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schuelke, Heiner Stuckenschmidt

Getting started

Install all packages from requirements.txt

What to run

• src/write_baseline_rules.py # to write for each dataset the baseline rules to rules/dataset_name/1_r.json. this is only needed for new datasets.
• optional: parameter_learning.py # to select the best values for alpha and lmbda_psi for each dataset for each relation; is stores in ./configs
• optional: parameter_learning_per_ds.py # to select the best values for alpha and lmbda_psi for each dataset and all relations ./configs
• test.py: to apply the baselines on the test set and compute final mrr and results file; results file is stored in ./results/dataset_name
• src/evaluation/run_evaluation.py

How to run

• see run.sh for examples how to run and reproduce our experiments.
• comment or uncomment the desired lines

How to evaluate

• see /src/evaluation for instructions

How to cite

@inproceedings{gastinger2024baselines,
  title={History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting},
  author={Gastinger, Julia and Meilicke, Christian and Errica, Federico and Sztyler, Timo and Schuelke, Anett and Stuckenschmidt, Heiner},
  booktitle={33nd International Joint Conference on Artificial Intelligence (IJCAI 2024)},
  year={2024},
  organization={International Joint Conferences on Artificial Intelligence Organization}
}