TorusE¶

Introduction¶

Title: TorusE: Knowledge Graph Embedding on a Lie Group

Authors: Takuma Ebisu, Ryutaro Ichise

Abstract: TorusE extends TransE by embedding entities and relations on a torus (a Lie group). This allows the model to avoid the regularization issues that TransE faces and provides a more principled way of handling the translation-based scoring function. The torus structure naturally handles the periodicity.

Running with hopwise¶

Model Hyper-Parameters:

embedding_size (int) : The embedding size of entities and relations. Defaults to 64.

A Running Example:

Write the following code to a python file, such as run.py

from hopwise.quick_start import run_hopwise

run_hopwise(model='TorusE', dataset='ml-100k')

And then:

python run.py

Tuning Hyper Parameters¶

If you want to use HyperTuning to tune hyper parameters of this model, you can copy the following settings and name it as hyper.test.

learning_rate choice [0.01,0.005,0.001,0.0005,0.0001]
embedding_size choice [32,64,128]

Note that we just provide these hyper parameter ranges for reference only, and we can not guarantee that they are the optimal range of this model.

Then, with the source code of hopwise (you can download it from GitHub), you can run the run_hyper.py to tuning:

hopwise tune --config_files=[config_files_path] --params_file=hyper.test

For more details about Parameter Tuning, refer to Parameter Tuning.