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TKG-Forecasting-Evaluation

TKG Forecasting Evaluation Paper

Please Cite our Paper: Julia Gastinger, Timo Sztyler, Lokesh Sharma, Anett Schuelke, Heiner Stuckenschmidt. Comparing Apples and Oranges? On the Evaluation of Methods for Temporal Knowledge Graphs. In ECML PKDD, Torino, Italy, 2023. link

or, older version:

Julia Gastinger, Timo Sztyler, Lokesh Sharma, Anett Schuelke. On the Evaluation of Methods for Temporal Knowledge Graph Forecasting. In Temporal Graph Learning Workshop (TGL 2022), NeurIPS, New Orleans, United States of America, 2022. https://openreview.net/pdf?id=J_SNklR-KR

Supplementary material: Please find the pdf with supplementary material in our github files: https://github.com/nec-research/TKG-Forecasting-Evaluation/blob/main/paper_supplementary_material.pdf

Clone (including submodules with forked and modified orignal models)

git clone --recursive https://github.com/nec-research/TKG-Forecasting-Evaluation.git

Requirements

• for each model create a conda environment, with the following names: xerte, regcn, renet, titer, tango, cygnet, tlogic
• for each conda environment install the required packages as described by each method, see each repos requirements.txt
• for general evaluation: torch, numpy, os, time

Set Up Experiments

• make sure to uncomment each model of interest in run_exp.sh and select the datasets of interest, as specified in the comments. For example:

 python3 run.py --gpu 1 --model 4 --num_seeds 1 --exp_name_int 0 --dataset_ids 1 3 4 5 6

• if desired: check the desired hyperparameters and evaluation settings in run.py. multi-step and single-step setting can be set for each method with feedgt_list = [False, True]. False means multi-step, and True means single-step
• Create a folder "Results" for each model directory

Run Experiments

• run ./run_exp.sh

Additional Information

• Each Models datasets are stored in the respective Models folder
• Experiments might run for long time, with total runtimes of multiple weeks
• Be aware that some models have high (GPU) memory requirements, especially for the datasets GDELT, ICEWS05-15 and WIKI

Evaluation

• See Readme in result_evaluation

Testing for xERTE

for xERTE:

• modify xERTE/tKGR/load_and_test.py according to the comments (A), (B), (C) to specify dictionaries and best epochs
• run xERTE/tKGR/load_and_test.py

Add new model

• Copy Code to this folder or, ideally, create a git submodule of a fork of the respective repository
• Add the datasets to the Code Folder
• Create a conda environment and install all dependencies provided by the original authors in this environment
• Make sure to fulfill all items from the checklist in paper, supplementary material
• Log the scores for each test query as implemented in the other models (see git diff) during testing, to a .pkl file, with keys: querys, values: scores and gt. For logging the scores your can use the methods as provided in evaluation_utils.py
• Add the model and hyperparameters to run.py (in eval() you need to add the model to the d_dict, and add an elif model == 'newmodel': .... ideally, you set the model args in get_arguments_list())
• Add the model and settings to run_exp.sh For evaluation of the new model:
• Follow steps in the results_evaluation Readme

Dataset Sources

Copied from RE-GCN (https://github.com/Lee-zix/RE-GCN)

• ICEWS18:Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. Recurrent event network: Autoregressive structure inference over temporal knowledge graphs. arXiv preprint arXiv:1904.05530, 2019. preprint version.
• GDELT: Kalev Leetaru and Philip A Schrodt. Gdelt: Global data on events, location, and tone, 1979–2012. In ISA annual convention, pages 1–49. Citeseer, 2013.
• YAGO: Farzaneh Mahdisoltani, Joanna Asia Biega, and Fabian M. Suchanek. Yago3: A knowledge base from multilingual wikipedias. In CIDR, 2015.
• WIKI: Julien Leblay and Melisachew Wudage Chekol. Deriving validity time in knowledge graph. In Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and Panagiotis G. Ipeirotis, editors, Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018, pages 1771–1776. ACM, 2018.
• ICEWS05-15 and ICEWS14: Alberto García-Durán, Sebastijan Dumanˇci´c, and Mathias Niepert. Learning sequence encoders for temporal knowledge graph completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4816–4821, Brussels, Belgium, October-November 2018. Association for Computational Linguistics.