Awesome
GoldE
Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
(This paper is accepted by ICML 2024)
Requirements
- pytorch == 1.8.0
- numpy == 1.19.2
- scikit-learn == 0.23.2
Data
- entities.dict: a dictionary map entities to unique ids
- relations.dict: a dictionary map relations to unique ids
- train.txt: the KGE model is trained to fit this data set
- valid.txt: create a blank file if no validation data is available
- test.txt: the KGE model is evaluated on this data set
Usage
All training commands are listed in best_config.sh. For example, you can run the following commands to train GoldE on WN18RR datasets.
# WN18RR
bash run.sh GoldE wn18rr 0 0 0 1000 200 800 12 10 0.666435178264418 0.99 0.5 6.0 1.1 0.003 60000 20000 16 0.185933138885153 -sf
Acknowledgement
We refer to the code of RotatE and HousE. Thanks for their contributions.