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ParTrans-X

ParTrans-X is an efficient parallel lib for translating embedding algorithms.

The main adavantage of our lib is that our algorithms are totally lock-free multithreading, which enables the algorithms to be highly efficient, and we give some theoretical guarantees in our paper.

Up to now, we implemented the parallel version of TransE, AdagradTransE, and TransH. Named ParTransE, ParAdagradTransE, ParTransH respectively.

Also, we implemented the parallel version of the link prediction program for each of them, named ParTest_TransE and ParTest_TransH. (TransE and AdagradTransE use the same test program).

In addition, we provide all the shell scripts for parameter tuning.

Evaluation Results

We list the link prediction result and runtime of various methods implemented by ourselves in dateset FB15k and WN18.

FB15k,nepoch=1000(100 for AdagradTransE), threads=20

Model       MeanRank(Raw)   MeanRank(Filter)   Hit@10(Raw) Hit@10(Filter) Time(s) Speedup Ratio
TransE 184 73 44.5 60.7 4658 -
ParTransE 185 69 45.3 62.3 496 9
ParAdagradTransE 186 70 44.9 61.9 42 111
TransH 183 60 46.6 65.5 6066 -
ParTransH 183 60 46.8 65.7 474 13

WN18,nepoch=1000(100 for AdagradTransE), threads=20

Model       MeanRank(Raw)   MeanRank(Filter)   Hit@10(Raw) Hit@10(Filter) Time(s) Speedup Ratio
TransE 214 203 58.2 65.9 473 -
ParTransE 217 206 55.7 63.1 54 9
ParAdagradTransE 219 208 67.7 76.2 17 28
TransH 227 216 66.5 75.9 637 -
ParTransH 215 203 66.8 76.6 134 4.8

Compile and Use

Since we use openmp for multithreading, your g++ should contain it.

compile eg:

g++ -fopenmp ParTransE.cpp -o ParTransE

g++ -fopenmp ParTest_TransE.cpp -o ParTest_TransE

Use the script for parameter tuning is quite easy:

nohup ./train_test_TransE > logs 2>&1 &

More specific details can be found in the shell scripts, you can also make customized scripts.

Notice:

You should set $coreNum in the shell script less than the max number of your cores in your machine.

Cite

If you use the code, please kindly cite the following paper:

Denghui Zhang, Manling Li, Yantao Jia, Yuanzhuo Wang. Efficient Parallel Translating Embedding For Knowledge Graphs.IEEE/WIC/ACM International Conference on Web Intelligence 2017 (WI 2017).[pdf]

@inproceedings{zhang2017efficient,
  title={Efficient parallel translating embedding for knowledge graphs},
  author={Zhang, Denghui and Li, Manling and Jia, Yantao and Wang, Yuanzhuo and Cheng, Xueqi},
  booktitle={Proceedings of the International Conference on Web Intelligence},
  pages={460--468},
  year={2017}
}

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Parallel TransE and the variants

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