DGL-KE Command Lines

DGL-KE provides a set of command line tools to train knowledge graph embeddings and make prediction with the embeddings easily.

Commands for Training

DGL-KE provides commands to support training on CPUs, GPUs in a single machine and a cluster of machines.

dglke_train trains KG embeddings on CPUs or GPUs in a single machine and saves the trained node embeddings and relation embeddings on disks.

dglke_dist_train trains knowledge graph embeddings on a cluster of machines. This command launches a set of processes to perform distributed training automatically.

To support distributed training, DGL-KE provides a command to partition a knowledge graph before training.

dglke_partition partitions the given knowledge graph into N parts by the METIS partition algorithm. Different partitions will be stored on different machines in distributed training. You can find more details about the METIS partition algorithm in this link.

In addition, DGL-kE provides a command to evaluate the quality of pre-trained embeddings.

dglke_eval reads the pre-trained embeddings and evaluates the quality of the embeddings with a link prediction task on the test set.

Commands for Inference

DGL-KE supports two types of inference tasks using pretained embeddings (We recommand using DGL-KE to generate these embedding).

• Predicting entities/relations in a triplet Given entities and/or relations, predict which entities or relations are likely to connect with the existing entities for given relations. For example, given a head entity and a relation, predict which entities are likely to connect to the head entity via the given relation.
• Finding similar embeddings Given entity/relation embeddings, find the most similar entity/relation embeddings for some pre-defined similarity functions.

The ranking result will be automatically stored in the output file (result.tsv by default) using the tsv format. DGL-KE provides two commands for the inference tasks:

dglke_predict predicts missing entities/relations in triplets using the pre-trained embeddings.

dglke_emb_sim computes similarity scores on the entity embeddings or relation embeddings.