Analogy

Introduction

[paper]

Title: Analogical Inference for Multi-Relational Embeddings

Authors: Hanxiao Liu, Yuexin Wu, Yiming Yang

Abstract: Analogy learns entity and relation embeddings that support analogical inference. The model is designed to capture analogical structures in knowledge graphs, where relations between entity pairs can be characterized by analogies (e.g., king:queen :: man:woman).

Running with hopwise

Model Hyper-Parameters:

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

  • scalar_share (float) : Scalar share parameter. Defaults to 0.5.

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='Analogy', 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]
scalar_share choice [0.3,0.5,0.7]

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.