Update README.md

Author: mana-ysh

README.md

@@ -2,8 +2,16 @@
 
 Python Implementations of Embedding-based methods for Knowledge Base Completion tasks, mainly inspired by [scikit-kge](https://github.com/mnick/scikit-kge) and [complex](https://github.com/ttrouill/complex).
 
-Currently, This repository contains the reimplementation of Complex Embeddings [Trouillon el al. 2016].   
-I plan to add the other baseline methods, such as TransE, RESCAL or Holographic Embeddings. 
+## List of methods
+- RESCAL [Nickel+. 2011]
+- TransE [Bordes+. 2013]
+- DistMult [Yang+. 2015]
+- HolE [Nicklel+. 2016] 
+  - This model is equivalent to ComplEx, and the computation cost for ComplEx is lower than of HolE.
+- ComplEx [Trouillon+. 2016]
+- ANALOGY [Liu+. 2017] (not implemented yet)
+  - This model can be regarded as a hybrid between DistMult and RESCAL.
+
 
 ## Run to train and test
 
@@ -28,7 +36,8 @@ optional arguments:
   --rel REL             relation list
   --train TRAIN         training data
   --valid VALID         validation data
-  --method METHOD       method ["complex"]
+  --method METHOD       method ["complex", "distmult", "transe", "hole",
+                        "rescal"]
   --epoch EPOCH         number of epochs
   --batch BATCH         batch size
   --lr LR               learning rate
@@ -63,7 +72,8 @@ optional arguments:
   --data DATA          test data
   --filtered           use filtered metric
   --graphall GRAPHALL  all graph file for filtered evaluation
-  --method METHOD      method ["complex"]
+  --method METHOD      method ["complex", "distmult", "transe", "hole",
+                       "rescal"]
   --model MODEL        trained model path
 ```
 
@@ -91,10 +101,14 @@ optional arguments:
 
 ## References
 
-* 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)
+* 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). 
 
-* 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).
+* 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.