An extremely fast implementation of TransE [1], TransH [2], TransR [3], TransD [4], TranSparse [5] for knowledge representation learning (KRL) based on our previous pakcage KB2E ("https://github.com/thunlp/KB2E") for KRL. The overall framework is similar to KB2E, with some underlying design changes for acceleration. This implementation also supports multi-threaded training to save time.
Because the overall framework is similar, we just list the result of transE(previous model) and new implemented models in dateset FB15k.
CPU : Intel Core i7-6700k 4.00GHz.
| Model | MeanRank(Raw) | MeanRank(Filter) | Hit@10(Raw) | Hit@10(Filter) | Time |
|---|---|---|---|---|---|
| TransE (n = 50, rounds = 1000) | 210 | 82 | 41.9 | 61.3 | 59m47s |
| Fast-TransE (n = 50, threads = 8, rounds = 1000) | 212 | 82 | 41.4 | 59.7 | 45s |
| Fast-TransH (n = 50, threads = 8, rounds = 1000) | 203 | 67 | 44.6 | 63.1 | 2m24s |
| Fast-TransR (n = 50, threads = 8, rounds = 1000) | 196 | 76 | 45.6 | 69.1 | 19m34s |
| Fast-TransD (n = 50, threads = 8, rounds = 1000) | 210 | 73 | 43.5 | 64 | 3m19s |
More results can be found in ("https://github.com/thunlp/KB2E").
Datasets are required in the following format, containing three files:
triple2id.txt: training file, the first line is the number of triples for training. Then the follow lines are all in the format (e1, e2, rel).
entity2id.txt: all entities and corresponding ids, one per line. The first line is the number of entities.
relation2id.txt: all relations and corresponding ids, one per line. The first line is the number of relations.
You can download FB15K from [Download], and the more datasets can also be found in ("https://github.com/thunlp/KB2E").
g++ transX.cpp -o transX -pthread -O3 -march=native
If you use the code, please kindly cite the following paper and other papers listed in our reference:
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. The 29th AAAI Conference on Artificial Intelligence (AAAI'15). [pdf]
[1] Bordes, Antoine, et al. Translating embeddings for modeling multi-relational data. Proceedings of NIPS, 2013.
[2] Zhen Wang, Jianwen Zhang, et al. Knowledge Graph Embedding by Translating on Hyperplanes. Proceedings of AAAI, 2014.
[3] Yankai Lin, Zhiyuan Liu, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of AAAI, 2015.
[4] Guoliang Ji, Shizhu He, et al. Knowledge Graph Embedding via Dynamic Mapping Matrix. Proceedings of ACL, 2015.
[5] Guoliang Ji, Kang Liu, et al. Knowledge Graph Completion with Adaptive Sparse Transfer Matrix. Proceedings of AAAI, 2016.