Heads-up: The code in this repo is functional, reliable, but also, well... ugly. Today I would probably not write this kind of code anymore. So, proceed at your own risk!

BoxTE is a box embedding model for temporal knowledge graph completion (TKGC), developed by Ralph Abboud, Ismail Ilkan Ceylan, and myself. It achieves state-of-the art performance on multiple TKGC benchmarks, while being fully expressive, inherently interpretable, and capturing various logical inference patterns. Get the AAAI paper here: https://arxiv.org/abs/2109.08970

This repository contains the source code for the BoxTE embedding model and additionally contains scripts for training and testing, as well TKGC datasets.

• PyTorch >= 1.7.0 and corresponding NumPy version

To train the BoxTE model, run main.py and specify the required arguments --train_path, --test_path and --valid_path to select a dataset. The flag -h can be used to obtain a description of all available settings: python main.py -h. Using these, different hyperparameter-settings and model variants can be selected.

To perform a test on saved/pretrained model parameters, run main.py, specify --load_params_path and set --num_epochs=0.

We provide hyperparameter-files that contain the settings used to obtain best results on each dataset. To run experiments with these settings, execute the following commands from within the repository:

python main.py @path/to/repo/modelargs/icews14

python main.py @path/to/repo/modelargs/icews5-15

python main.py @path/to/repo/modelargs/gdelt

To reproduce the results in a setting with a limited number of model parameters, run:

python main.py @path/to/repo/modelargs/icews14-lowdim

python main.py @path/to/repo/modelargs/icews5-15-lowdim

python main.py @path/to/repo/modelargs/gdelt-lowdim