Python Implementations of Embedding-based methods for Knowledge Base Completion tasks, mainly inspired by scikit-kge and complex.

• RESCAL [Nickel+. 2011]
• TransE [Bordes+. 2013]
• DistMult [Yang+. 2015]
• HolE [Nicklel+. 2016]
  • This model is equivalent to ComplEx[Hayashi and Shimbo. 2018], and the computation cost of ComplEx is lower than of HolE.
• ComplEx [Trouillon+. 2016]
• ANALOGY [Liu+. 2017]
  • This model can be regarded as a hybrid between DistMult and ComplEx.

For training...

▶ python train.py -h usage: Link prediction models [-h] [--mode MODE] [--ent ENT] [--rel REL] [--train TRAIN] [--valid VALID] [--method METHOD] [--epoch EPOCH] [--batch BATCH] [--lr LR] [--dim DIM] [--margin MARGIN] [--negative NEGATIVE] [--opt OPT] [--l2_reg L2_REG] [--gradclip GRADCLIP] [--save_step SAVE_STEP] [--cp_ratio CP_RATIO] [--metric METRIC] [--nbest NBEST] [--filtered] [--graphall GRAPHALL] [--log LOG] optional arguments: -h, --help show this help message and exit --mode MODE training mode ["pairwise", "single"] --ent ENT entity list --rel REL relation list --train TRAIN training data --valid VALID validation data --method METHOD method ["complex", "distmult", "transe", "hole", "rescal", "analogy"] --epoch EPOCH number of epochs --batch BATCH batch size --lr LR learning rate --dim DIM dimension of embeddings --margin MARGIN margin in max-margin loss for pairwise training --negative NEGATIVE number of negative samples for pairwise training --opt OPT optimizer ["sgd", "adagrad"] --l2_reg L2_REG L2 regularization --gradclip GRADCLIP gradient clipping --save_step SAVE_STEP epoch step for saving model --cp_ratio CP_RATIO ratio of complex's dimention in ANALOGY --metric METRIC evaluation metrics ["mrr", "hits"] --nbest NBEST n-best for hits metric --filtered use filtered metric --graphall GRAPHALL all graph file for filtered evaluation --log LOG output log dir 

For testing...

▶ python test.py -h usage: Link prediction models [-h] [--ent ENT] [--rel REL] [--data DATA] [--filtered] [--graphall GRAPHALL] [--method METHOD] [--model MODEL] optional arguments: -h, --help show this help message and exit --ent ENT entity list --rel REL relation list --data DATA test data --filtered use filtered metric --graphall GRAPHALL all graph file for filtered evaluation --method METHOD method ["complex", "distmult", "transe", "hole", "rescal", "analogy"] --model MODEL trained model path 

hyper parameters

• mode : single
• epoch : 500
• batch : 128
• lr : 0.05
• dim : 200
• negative : 5
• opt : adagrad
• l2_reg : 0.001
• gradclip : 5

hyper parameters

• mode : single
• epoch : 500
• batch : 128
• lr : 0.05
• dim : 200
• negative : 10
• opt : adagrad
• l2_reg : 0.0001
• gradclip : 5

* means the results reported from the original papers

• numpy
• scipy

• Bordes, A.; Usunier, N.; Garcia-Duran, A.; Weston, J.; and Yakhnenko, O. 2013. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems (NIPS).

• Liu, H.; Wu, Y.; and Yang, Y. 2017. Analogical inference for multi-relational embeddings. In Proceedings of the 34th International Conference on Machine Learning (ICML).

• Nickel, M.; Rosasco, L.; and Poggio, T. 2016. Holographic embeddings of knowledge graphs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16.

• Nickel, M.; Tresp, V.; and Kriegel, H.-P. 2011. A threeway model for collective learning on multi-relational data. In International Conference on Machine Learning (ICML-11), ICML ’11,

• Trouillon, T.; Welbl, J.; Riedel, S.; Gaussier, E.; and Bouchard, G. 2016. Complex embeddings for simple link prediction. In International Conference on Machine Learning (ICML).

• Yang, B.; Yih, W.; He, X.; Gao, J.; and Deng, L. 2015. Embedding entities and relations for learning and inference in knowledge bases. International Conference on Learning Representations 2015.