Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks. Despite the rapid growth of knowledge construction, knowledge graphs still suffer from severe incompletion and inevitably involve various kinds of errors. Several attempts have been made to complete knowledge graph as well as to detect noise. However, none of them considers unifying these two tasks even though they are inter-dependent and can mutually boost the performance of each other. In this paper, we proposed to jointly combine these two tasks with a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding. Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms both in regard to knowledge graph completion and error detection.
This paper proposes to provide a novel noise-aware knowledge graph embedding (NoiGAN) by combining KG completion and noise detection through the GANs framework. More specifically, NoiGAN repeatedly utilizes a GAN model to 1) approximate the confidence score for facts identifying reliable data (discriminator) and 2) generate more challenging negative samples (generator). Then, it uses this confidence score and negative samples to train a more accurate link prediction model. The authors validate the proposed model through several experiments.
This paper reads well and the results appear sound. I personally find the idea of incorporating confidence score into a link prediction model to achieve a more accurate model very interesting. Furthermore, the provided experiments support their intuition and arguments outperforming considered baselines.
As for the drawbacks, I find the baselines considered in this work outdated missing many SOTA and related works in link prediction and noise detection [1,2, 3, 4, 5]. Further, I believe this work needs more experimental results and an ablation study capturing different aspects of the presented method. My concerns are as follows:
•	Considering the existing reverse relation issue in FB15K and WN18, I suggest conducting the experiments on the FB15K-237 and WN18RR from [6] instead. 
•	I suggest considering more recent link prediction models as baselines.
•	I am wondering if the only difference between NoiGAN and KBGAN [7] is incorporating the confidence score in the link prediction loss?
•	Considering the fact that NoiGAN repeatedly retrains GAN and link prediction model, I suggest providing a comparison of computational complexity.
•	I am wondering if NoiGAN can only work with pre-knowledge of noisy triples in KG? If not, why didn’t you report NoiGAN performance with 0% noise in Table 3?
•	I find utilizing few examples to evaluate the power of discriminator in distinguishing noisy triples (Table 4) not satisfactory at all. I suggest experimenting with more data and providing the per-relation breakdown performance of the discriminator.
On overall, although I find the proposed model quite novel and interesting, the paper needs more experimental results to validate the idea.
 
[1] Pinter, Yuval, and Jacob Eisenstein. "Predicting Semantic Relations using Global Graph Properties".
[2] Nathani, Deepak, et al. "Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs". 
[3] Balažević, Ivana, Carl Allen, and Timothy M. Hospedales. "TuckER: Tensor Factorization for Knowledge Graph Completion".
[4] Sun, Zhiqing, et al. "Rotate: Knowledge graph embedding by relational rotation in complex space".
[5] Pezeshkpour, Pouya, Yifan Tian, and Sameer Singh. "Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications".
[6] Dettmers, Tim, et al. "Convolutional 2d knowledge graph embeddings.", AAAI-18.
[7] Liwei Cai and William Yang Wang. “Kbgan: Adversarial learning for knowledge graph embeddings”.