Algorithm

"Improving Knowledge Graph Embedding Using Simple Constraints" (ACL-2018)

Parameters

You can changes parameter when training the model

 k = number of dimensions
 lmbda = L2 regularization coffecient
 neg = number of negative samples
 mu = AER regularization coffecient

Citation

 @inproceedings{boyang2018:aer,
 	author = {Ding, Boyang and Wang, Quan and Wang, Bin and Guo, Li},
 	booktitle = {56th Annual Meeting of the Association for Computational Linguistics},
 	title = {Improving Knowledge Graph Embedding Using Simple Constraints},
 	year = {2018}
 }

Contact

For all remarks or questions please contact Quan Wang: wangquan (at) iie (dot) ac (dot) cn .

• Improving Knowledge Graph Embedding Using Simple Constraints. Boyang Ding, Quan Wang, Bin Wang and Li Guo. ACL 2018.

• Complex Embeddings for Simple Link Prediction. Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier and Guillaume Bouchard. ICML 2016.
• Regularizing Knowledge Graph Embeddings via Equivalence And Inversion Axioms. Pasquale Minervini, Luca Costabello, Emir Muñoz, Vít Nováček And Pierre-Yves Vandenbussche. ECML 2017.