Knowledge Graph Embedding (KGE) is the task of jointly learning entity and relation embeddings for a given knowledge graph. Existing methods for learning KGEs can be seen as a two-stage process where (a) entities and relations in the knowledge graph are represented using some linear algebraic structures (embeddings), and (b) a scoring function is defined that evaluates the strength of a relation that holds between two entities using the corresponding relation and entity embeddings. Unfortunately, prior proposals for the scoring functions in the first step have been heuristically motivated, and it is unclear as to how the scoring functions in KGEs relate to the generation process of the underlying knowledge graph. To address this issue, we propose a generative account of the KGE learning task. Specifically, given a knowledge graph represented by a set of relational triples (h, R, t), where the semantic relation R holds between the two entities h (head) and t (tail), we extend the random walk model (Arora et al., 2016a) of word embeddings to KGE. We derive a theoretical relationship between the joint probability p(h, R, t) and the embeddings of h, R and t. Moreover, we show that marginal loss minimisation, a popular objective used by much prior work in KGE, follows naturally from the log-likelihood ratio maximisation under the probabilities estimated from the KGEs according to our theoretical relationship. We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. The KGEs learnt by our proposed method obtain state-of-the-art performance on FB15K237 and WN18RR benchmark datasets, providing empirical evidence in support of the theory.
This paper proposes to perform the link prediction in knowledge bases by introducing a new scoring function and theoretically motivating their method. The authors validate their proposed approach through several experiments. 
This paper reads well and the results appear sound. I personally find the theoretical argument behind the proposed scoring function very interesting. Unfortunately, the contribution seems rather small to be accepted for ICLR. This is a straight application and combination of existing pieces with not much originality and without being backed up by very strong experimental results. My concerns are as follows:
   - Having only results on two flawed datasets (considering the inverse relations in them) makes it hard to evaluate the quality of the method. I suggest conducting the experiments on the FB15K-237 and WN18RR from [1] instead. 
   - You only evaluate on MR and Hits@10, but it is standard to include metrics like MRR and Hits@1 and 3 also, since no metric is perfect for this task.
   - Since the goal of the work is not providing a state of the art method, and focus on the theoretical understanding of their scoring function, it is of high importance to assess the characteristics of their embeddings and scoring function through designing other experiments. As a result, I suggest to study the geometric behavior of their embeddings and compare it to the other methods. Further, investigating the semantic purity of the embeddings by calculating the entropy of the type distribution of the entities, similar as [2], can shed more light on the significance of their method.
On overall, although the proposed method seems a direct application of Arora et al.,2016a, I find their extension novel and quite interesting, But the paper needs more experimental results to validate the idea.  
[1] Dettmers, Tim, et al. "Convolutional 2d knowledge graph embeddings.", AAAI-18.
[2] Ding, Boyang, et al. "Improving Knowledge Graph Embedding Using Simple Constraints.", ACL-18.