ParaGraphE provides a library for parallel knowledge graph embedding, which implements several knowledge graph embedding methods on a single machine with multiple cores and a shared memory. The implemented methods in the current version include TransE[1], TransH[2], TransR[3], TransD[4], and SphereE (the sphere method in ManifoldE)[5].

ParaGraphE aims at accelerating the speed of training and testing of knowledge graph embedding methods. We re-implement these methods in a multi-thread way, which achieves a significant time reduction without influencing the accuracy. The parallel optimization/learning algorithms of ParaGraphE are based on the lock-free strategies in [6,7].

More details about ParaGraphR can be found at the arXiv draft[8].


We include the Makefile in this folder, just type “make” in command line to compile the code.


To start training, you can input a command like this:

./train -nthreads 10 -method TransE -path data/WN18 -dim 50 -margin 4 -nepoches 1000 -nbatches 1 -use_tmp 0 

Several parameters can be set by the command line. They are:

-nthreads : The number of threads for executing. (default: 10)

-path: The path of data. (default: data/FB15k)

-method	: The method of embedding. Choice: TransE, TransH, TransR, TransD, SphereE. (default: TransE)
-nepoches : The number of epoches for training. (default: 1000)

-dim : The dimension of embeddings. (default: 50)

-rate : Learning rate. (default: 0.01)

-margin: The margin parameter in rank-based hinge loss. (default: 1)

-l1\_norm : Use L1 norm in training. Choice: 0:L2\_norm, 1:L1\_norm. (default: 1)

-corr_method : Use "unif" or "bern" for sampling corrupted triples. Choice: 0:unif, 1:bern. (default: 0)

-nbatches: The number of batches in an epoch. Need to cooperate with "-use_tmp 1" to employ minibatch training. According to our experience, it's performance is usually not good due to asynchronous update in	multi-thread setting. So minibatch is not recommended. (default: 1)

-use_tmp : Whether to update parameters after a batch. (default: 0)

-init_from_file : Whether to init parameters from TransE result. TransH, TransR, TransD can have a perfermance boost initialized from TransE result. ***Make sure you have already run TransE on this dataset once and have saved the result in that data path before you use this command.*** (default: 0)

-orth_value (only in TransH) : The orthogonal parameter \episilon in TransH. (default: 0.1)

-dim2 (only in TransR) : The dimension of relational space in TransR. (default: equal to -dim)

After training with a notice “the embeddings have already been saved in files.”, embeddings are saved in the same data path of that dataset. For example, after you run TransE on data/WN18, you can find two files named “TransE_entity_vec.txt” and “TransE_relation_vec.txt” in that path. Those files can be read by the testing program during the testing procedure.


After you have saved embeddings in files, you can start testing with a command like this:

./test -nthreads 10 -method TransE -path data/WN18 -l1_norm 1

Several parameters can be set by the command line. They are:

-nthreads : The number of threads for executing. (default: 10)

-path: The path of data. (default: data/FB15k)

-method	: The method of embedding. Choice: TransE, TransH, TransR, TransD, SphereE. (default: TransE)

-l1\_norm : Use L1 norm in testing. Choice: 0:L2\_norm, 1:L1_norm. (default: 1)

The program will report the result in several popular metrics of knowledge graph embedding. They are:

mr : The value of mean rank.

mrr : The value of mean reciprocal rank.

hits@10 : The proportion of ranks no larger than 10.

hits@1 : The porportion of ranks list at first.

In the output, those metrics with a “f_” mark before them are the results in “filter” settings. And those without “f_” are the results in “raw” settings.

Implementing customized method

You can easily implement your own method based on our framework. Simply write a class by inheriting the class transbase, and then implement a construction function and the following pure virtual functions in transbase:

void initial() : How to initialize parameters. double triple_loss(int h, int r, int t) : Calculating the loss of triple <h,r,t>. void gradient(int r, int h, int t, int h_, int t_) : Doing gradient descent based on gold triple <h,r,t> and corrupted triple <h_, r, t_>. void save_to_file(string data_path) : Save embeddings into files. void read_from_file(string data_path) : Read embeddings from files when testing. void batch_update() : Useful in use_tmp = 1. Update the parameters.

Then add one or two lines of code in train.cpp and test.cpp by following similar ways in our implemented methods. Modify Makefile and re-compile the program, then your method is ready to run.


[1] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko. Translating Embeddings for Modeling Multi-relational Data. Proceedings of the 28th Advances in Neural Information Processing Systems (NIPS), 2013.

[2] Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen. Knowledge Graph Embedding by Translating on Hyperplanes. Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), 2014.

[3] Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015.

[4] Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zhao. Knowledge Graph Embedding via Dynamic Mapping Matrix. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), 2015.

[5] Han Xiao, Minlie Huang, Xiaoyan Zhu. From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016.

[6] Benjamin Recht, Christopher Ré, Stephen J. Wright, Feng Niu. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. Proceedings of the 25th Annual Conference on Neural Information Processing Systems (NIPS), 2011.

[7] Shen-Yi Zhao, Gong-Duo Zhang, Wu-Jun Li. Lock-Free Optimization for Non-Convex Problems. Proceedings of the 31th AAAIConference on Artificial Intelligence (AAAI), 2017.

[8] Xiao-Fan Niu, Wu-Jun Li. ParaGraphE: A Library for Parallel Knowledge Graph Embedding. arXiv. 2017.

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