Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: * The paper presents a novel way of performing few-shot KB completion without having to rely on meta-train datasets constructed in an ad-hoc fashion. Specifically, they propose learning a hypothesis proposal module that given different support evidence graphs, finds a common hypothesis that is supported by the evidence. The hypothesis is then instantiated with the different examples and passed to the encoder to generate a representation of the support examples. During inference, the proposed method checks if there is an evidence close enough to the proposed hypothesis using the Evidence Proposal module to first generate a hypothesis, and then instantiating it with the query, and using the encoder to generate the query evidence representation. 
* The authors present 2 approaches for the hypothesis proposal and evidence proposal modules: an optimization based training free method, and a fully trainable GCNN approach. For the latter, they present a self-supervised pre-training method for training. 
* The optimization free method is competitive against other Meta learning approaches, while the fully trainable approach outperforms them, especially for the scenario where inference is performed on unseen entities (dubbed the inductive setting in the paper).  
strengths_and_weaknesses: Strengths:

* The paper presents an interesting approach for adapting to few-shot KB completion, which is contrary to the popular meta-learning approach.
* It is especially interesting to see that the training free approach, with randomly initialized encoders performs competitively against other Meta learning approaches.
 * The results especially for the transductive setting for ConceptNet and NELL, and inductive setting for all 3 datasets, are quite compelling with substantial improvements over the baseline. In addition, the approach also appears to be fairly robust to distribution shifts.

Weakness
* Given that during inference, for each entity pair, the model needs to compute a forward pass through Evidence Proposal Module and the Encoder Module, it would be good to also compare the inference time of the proposed method compared to the baseline approaches. 
* Since the approach should produce interpretable masks, it might be helpful and insightful to include some qualitative examples. Eg: for the running example in the paper (chop, <>, kitchen), does the hypothesis proposal module actually identify the underlying accurate hypothesis, and is that correctly re-generated from the Evidence Proposal module. 
* The definition of pre-training is a bit overloaded. From the Supplementary material, it seems like the model is jointly trained with all 3 losses, as opposed to first doing self-supervised pre-training followed by finetuning. It might be good to clarify that in the paper. 
* [Minor] This setup for few-shot link prediction seems to be very well suited for in-context learning setups. I wonder if it makes sense to also compare against the same. 
* [Minor] The paper is a bit hard to parse: some of the notation (eg: Equations 9 and 11) are somewhat non-standard, in Equation 16, T is not defined, and it is somewhat unclear how all the pieces fit together. It would be nice if the authors could present (even in the Appendix, if space is a concern) an Algorithm that clearly defines how the different proposals are connected for a training and inference pass through the dataset. 
questions: Questions and Typographic improvements:
* Is there a hypothesis why even randomly initialized encoders work well with the training free approach. On a similar line of reasoning, would it be possible to get the results of using the training free approach, but leveraging the encoder trained with the learning based scheme ?
* For Algorithm 1, I think line 8 is incorrectly placed ? (It should be placed after line 6, if my understanding is correct)
* In Line 62, cook => chop ?
* For the Synthetic dataset experiment, it would be good to include some more details in the main paper from the appendix.
limitations: The authors have covered the limitations fairly well. I am a little concerned w.r.t inference cost of the proposed method. If it is indeed an issue, it would also be good to mention that in the limitations as well.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: In this paper, the authors investigated few-shot knowledge graph completion task. Previous work usually model this under meta-learning framework. The authors proposed to leverage sub-graph between the head entity and tail entity, which may be shared between support and query triplets. Through this way, they are able to better predict the target entity through the shared subgraph, which contains richer local information that the model can leverage. To get a model that can be generalized to specific KG, they proposed to pretrained encoder-decoder model with the pretraining task to recover the masked nodes and relations in the subgraph. The experimental verify the effectiveness of the proposed model, which outperforms the baseline models on multiple existing datasets dramatically.
strengths_and_weaknesses: Strengths:
* The authors proposed an approach to leverage local subgraph for few-shot relational reasoning tasks, which achieves state-of-the-art performance on multiple datasets.
* They proposed to pretrain an encoder-decoder model which can generalize to specific KG.
* The paper is well-written and easy to follow.

Weaknesses:
On some datasets like FB15K-237, the proposed approach underperforms an existing baseline model, which may raise the question of the generalization ability of the pretrained model and on what kind of datasets will this model performs better. 
questions: What if there are no relationship between  subgraphs in support and query sets, will the pretrained model still work properly?
limitations: N/A
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The author proposes  a method that can perform well on any novel few-shot tasks without relying on specifically designed meta-training set.
The proposed approach relies on an intuition that entities linked by the same relation should have a similar-strctured subgraph.
The framework is called connection subgraph reasoner, it has two major components: hypothesis proposal module and evidence proposal module. The hypothesis proposal module generates a hypothesis embedding from each support graph such that they are most similar to each other. Then, the evidence model attempts to find the closest evidence to the hypothesis embedding.
In a leanring-free optimization, the output masks are optimized directly. 
There's also a learning version where the masks are outputed by a neural model.
In experiments strong performance is shown. Especially in inductive setting.
strengths_and_weaknesses: The proposed method is novel.

I think the idea of subgraph matching is interesting. 

In experiments strong performance is shown. Especially in  inductive setting.
questions: Some part of the paper is not easy to follow. (see my questions)
limitations: I did not find a limitation section in the main text.
review_title: null

Meta-review: metareview: This paper studies few-shot knowledge graph completion problem. It proposes learning a hypothesis proposal module that given different support evidence graphs, finds a common hypothesis that is supported by the evidence. The authors present 2 approaches for the hypothesis proposal and evidence proposal modules: an optimization-based training free method, and a fully trainable GCNN approach. 

The reviewers agree that the proposed method is interesting and solid, the experiments are thorough, and the results provide valuable insights for future work. Reviewers' raised concerns and questions are properly addressed by the author's response. 
confidence: Certain
Decision: Accept