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. .. toctree:: :hidden: :maxdepth: 1 :titlesonly: format_kg format_out train partition dist_train eval predict emb_sim 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`__. .. __: http://glaros.dtc.umn.edu/gkhome/metis/metis/overview 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.