ProcrustEs-KGE

Code for Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis

Tested environment

• numpy==1.18.5

• torch==1.6.0

• experiment_impact_tracker==0.1.8

• scikit_learn==0.23.2

• NVIDIA GTX 1080 Ti GPU + Intel Core i9-9900K CPU

Usage

Train

An example command:

python3 run_train.py --cuda --data_path path/to/KG/data -lr 0.05 -td 2000 -sd 20 -save /some/where/

where lr is for the learning rate, td is for the total number of dimensions, and sd is the number of dimensions in a sub-space (NB: sd|td).

Test

Please use model_test.py to extend the code of RotatE. All instructions are consistent except the additional self.td and self.sd should be added (NB: need to keep the training configs).

Visualise

We provide an interactive demo for the 3D PCA result of trained WN18RR entity embeddings (Fig. 4 in the paper).

About

If you like our project or find it useful, please give us a ⭐ and cite us

@inproceedings{ProcrustEs-KGE,
    title = "Highly Efficient Knowledge Graph Embedding Learning with {O}rthogonal {P}rocrustes {A}nalysis",
    author = "Peng, Xutan and
      Chen, Guanyi  and
      Lin, Chenghua  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.187",
    pages = "2364--2375"
}