Neural Variational Inference For Embedding Knowledge Graphs
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. In this paper, we introduce two generic Variational Inference frameworks for generative models of Knowledge Graphs; Latent Fact Model and Latent Information Model. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions. The new framework can create models able to discover underlying probabilistic semantics for the symbolic representation by utilising parameterisable distributions which permit training by back-propagation in the context of neural variational inference, resulting in a highly-scalable method. Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduces training time at a cost of an additional approximation to the variational lower bound. The generative frameworks are flexible enough to allow training under any prior distribution that permits a re-parametrisation trick, as well as under any scoring function that permits maximum likelihood estimation of the parameters. Experiment results display the potential and efficiency of this framework by improving upon multiple benchmarks with Gaussian prior representations. Code publicly available on Github.
 * The paper proposed a neural variational inference framework for knowledge graph embedding. The paper proposed two models (Latent Fact Model and Latent Information Model) where the neural variational inference is carried out, with competitive results on standard datasets (WN18 and FB15K). * I am not fully convinced of the advantage of this variational inference approach compared with the optimization approach used in TransE, TransG, DistMult and ComplEx. As can be seen in Table 1, the best performance on WN18-RR and FB15K-257 are obtained without variational inference. In Table 2, performance of the variational inference approach is not as good as other approaches under the MR or Raw Hits metrics. Moreover, performance on FB15K is not reported in Table 2, which makes the result not as complete as Table 1. * It seems that the main difference between Latent Fact Model and Latent Information Model is the way prior is imposed on h. The authors may want to explain in plain language the differences and the motivation behind that. * It is unclear how the (.tau,y) labeled triples are generated, especially for the negative examples with y=0. Is it obtained by randomly corrupting the triples in the knowledge base, as done in other work? It would be better to make this point clear.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Variational Inference For Embedding Knowledge Graphs
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. In this paper, we introduce two generic Variational Inference frameworks for generative models of Knowledge Graphs; Latent Fact Model and Latent Information Model. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions. The new framework can create models able to discover underlying probabilistic semantics for the symbolic representation by utilising parameterisable distributions which permit training by back-propagation in the context of neural variational inference, resulting in a highly-scalable method. Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduces training time at a cost of an additional approximation to the variational lower bound. The generative frameworks are flexible enough to allow training under any prior distribution that permits a re-parametrisation trick, as well as under any scoring function that permits maximum likelihood estimation of the parameters. Experiment results display the potential and efficiency of this framework by improving upon multiple benchmarks with Gaussian prior representations. Code publicly available on Github.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 * The paper proposed a neural variational inference framework for knowledge graph embedding. The paper proposed two models (Latent Fact Model and Latent Information Model) where the neural variational inference is carried out, with competitive results on standard datasets (WN18 and FB15K). * I am not fully convinced of the advantage of this variational inference approach compared with the optimization approach used in TransE, TransG, DistMult and ComplEx. As can be seen in Table 1, the best performance on WN18-RR and FB15K-257 are obtained without variational inference. In Table 2, performance of the variational inference approach is not as good as other approaches under the MR or Raw Hits metrics. Moreover, performance on FB15K is not reported in Table 2, which makes the result not as complete as Table 1. * It seems that the main difference between Latent Fact Model and Latent Information Model is the way prior is imposed on h. The authors may want to explain in plain language the differences and the motivation behind that. * It is unclear how the (.tau,y) labeled triples are generated, especially for the negative examples with y=0. Is it obtained by randomly corrupting the triples in the knowledge base, as done in other work? It would be better to make this point clear.
assistant
 * The paper proposed a neural variational inference framework for knowledge graph embedding. The paper proposed two models (Latent Fact Model and Latent Information Model) where the neural variational inference is carried out, with competitive results on standard datasets (WN18 and FB15K). * I am not fully convinced of the advantage of this variational inference approach compared with the optimization approach used in TransE, TransG, DistMult and ComplEx. As can be seen in Table 1, the best performance on WN18-RR and FB15K-257 are obtained without variational inference. In Table 2, performance of the variational inference approach is not as good as other approaches under the MR or Raw Hits metrics. Moreover, performance on FB15K is not reported in Table 2, which makes the result not as complete as Table 1. * It seems that the main difference between Latent Fact Model and Latent Information Model is the way prior is imposed on h. The authors may want to explain in plain language the differences and the motivation behind that. * It is unclear how the (.tau,y) labeled triples are generated, especially for the negative examples with y=0. Is it obtained by randomly corrupting the triples in the knowledge base, as done in other work? It would be better to make this point clear.