How to Train

To compute a knowledge graph embedding, first import datasets and set configure parameters for training, then train models and export results. For instance, we write an example_train_transe.py to train TransE:

import config
import numpy as np

con = config.Config()
#Input training files from benchmarks/FB15K/ folder.
con.set_in_path("./benchmarks/FB15K/")

con.set_work_threads(4)
con.set_train_times(500)
con.set_nbatches(100)
con.set_alpha(0.001)
con.set_margin(1.0)
con.set_bern(0)
con.set_dimension(50)
con.set_ent_neg_rate(1)
con.set_rel_neg_rate(0)
con.set_opt_method("SGD")

#Models will be exported via torch.save() automatically.
con.set_export_files("./res/model.vec.pt")
#Model parameters will be exported to json files automatically.
con.set_out_files("./res/embedding.vec.json")
#Initialize experimental settings.
con.init()
#Set the knowledge embedding model
con.set_model(models.TransE)
#Train the model.
con.run()

Step 1: Import Datasets

We set the path of datasets:

con.set_in_path("benchmarks/FB15K/")

We import knowledge graphs from benchmarks/FB15K/ folder. The data consists of three essential files mentioned before

• train2id.txt
• entity2id.txt
• relation2id.txt

Validation and test files are required and used to evaluate the training results, However, they are not indispensable for training:

con.set_work_threads(8)

Step 2: Set Configure Parameters

We set the parameters, including the data traversing rounds, learning rate, batch size, and dimensions of entity and relation embeddings:

con.set_train_times(500)
con.set_nbatches(100)
con.set_alpha(0.5)
con.set_dimension(200)
con.set_margin(1)

For negative sampling, we can corrupt entities and relations to construct negative triples. set_bern(0) will use the traditional sampling method, and set_bern(1) will use the method in (Wang et al. 2014) denoted as “bern”.

We can select a proper gradient descent optimization algorithm to train models:

con.set_optimizer("SGD")

Step 3: Export Results

Models will be exported via torch.save() automatically every few rounds. Also, model parameters will be exported to json files finally:

con.set_export_files("./res/model.vec.pt")

con.set_out_files("./res/embedding.vec.json")

Step 4: Train Models

We set the knowledge graph embedding model and start the training process:

con.init()
con.set_model(models.TransE)
con.run()