Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper presents a comprehensive method to model the hyper-relational knowledge graph. The authors transform a HKG into a KG by introducing extra entities and relations which constructing a one-to-one mapping between the constructed KG and the previous HKG. The transformation is information preserving which is intuitive and widely used in other research domain such as graph theory. The author then design cured  function to aggregate information for newly created entities. And feed into an Encoder-Decoder framework for model training. The experiment result for three datasets shows the improvement for such graph transformation.
strength_and_weaknesses: Strength:
1. The proposed method is intuitive and effective
2. The proposed graph transformation bridges the algorithms between KG modeling and HKG modeling.

Weaknesses:
1. In Sec 3.1.2 under the GNN-based Encoder. Initialized embedding alpha setting is kind of ad-hoc (grid search in the experiment). An insightful guidance for setting such hyper-params for new dataset will be helpful.
2. In Model Training part. The negative sampling procedure is unclear since the graph itself is not the graph original HKG task. The negative sampling procedure may influence the result.
3. Possibly unfair comparison in the experiment. In Sec 4.2, the author indicated the task in their setting is different from StarE, which include more predicting task. However, important detailed information is missing such as how they handle facts with different number of qualifiers, how they adapt the baseline which doesn't support predicting entities in qualifiers into their setting.
4. A lot of explanations are in appendix, which block the reader from following the main stream of the paper.
clarity,_quality,_novelty_and_reproducibility: The paper presentation is unclear in which more information is needed to judge the work.

Even though the method of creating a graph transformation is widely used in other domain, there is still novelty in this work to introduce it into HKG modeling.

The source code is released which the reproducibility can be easily tested.

summary_of_the_review: The proposed method is simple yet effective. The technical contribution is limited. The experiment settings omit a lot of information which prevent fair comparison. Hence I would recommend "Discussion" to get feedback from the authors.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: In this paper, the authors proposed a model named TransEQ for hyper-relational knowledge graph modeling. In the proposed pipeline, they first convert the hyper-relational knowledge graph into normal knowledge graphs by transforming hyper-relations into mediator entities and relations while keeping the structural and semantic information; then, a graph neural network based model is used for knowledge graph encoding, and hyper-relational knowledge graph scoring functions are used for prediction.  Experiments on three datasets demonstrate the effectiveness of TransEQ.

strength_and_weaknesses: ### Strength 
- The transformation idea is straightforward and well-motivated. The authors provide brief proof of the full expressivity yet it can be done in a more detailed way.
- They conducted thorough model analyses (complex analysis, comparison of model properties, hyperparameters, etc.)

### Weaknesses
- The expressiveness of the hyper-relational knowledge graph transformation is highly dependent on the choice of scoring function, which may limit the generalization capability.
- The transformation will produce a large number of additional edges and entities, making the learning and inference more expensive, as pointed out in section 3.2, they have to use parallel implementation to accelerate the process. 
-  Some baselines are missing; please refer to [a][b][c[. Regarding the baselines, The reviewer is confused about the performance of StarE; according to Table 2 in the StarE paper, the best performance of StarE is higher than the reported one in this paper. For example, the best performance on WikiPeople is (MRR: 0.491; H@1:0.415, H@10: 0.648); The best on JF17K are (MRR: 0.574; H@1:0.496, H@10: 0.725). It is noted that StarE outperforms the proposed model on all metrics.


Reference:
[a] Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases, WWW 2020
[b] Role-Aware Modeling for N-ary Relational Knowledge Bases, WWW 2021
[c] Searching to Sparsify Tensor Decomposition for N-ary Relational Data, WWW 2021
clarity,_quality,_novelty_and_reproducibility: I do have a few questions that may need clarification from the authors 
- The description of the learnable transformations is quite confusing. How can it be performed? Any empirical/initial results?
- Are there any meaningful interpretations of the generated mediator entities and edges?

Apart from that, many details w.r.t. the model architecture and experiment implementation are discussed in the paper, which is helpful for understanding the whole paper. Code is also available.

In terms of novelty, the reviewer did not see much novelty in the model design as most of the components are taken from existing work. The most novel contribution is the HKG to KG transformation method.

summary_of_the_review: To summarize, the paper is well-written and easy to follow. The HKG to KG transformation method is intuitive and easy to implement. However, the weaknesses of the paper cannot be ignored. The reviewer is concerned about the complexity, generalization capability, and correctness of the evaluation, which makes the reviewer give a negative recommendation.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: Studies link prediction for hyper-relational knowledge graphs (in which edges are associated with have attribute/entity pairs). Transforms the hyper-relational KG into a standard KG using reification, then runs a relational GNN for link prediction.

strength_and_weaknesses: Strengths:

S1. Simple approach (but see W1).

S2. Good experimental results (but see W2).

Weaknesses:

W1. Little novelty. The proposed transformation is equivalent to reification (e.g, as used in RDF, not discussed in paper) + keeping the original fact. The introduced blank nodes share (partial) embeddings with their subject entity, which is a good idea but also the immediate approach after a minute of thought. The GNNs being used are out-of-the-box relational GNNs.

W2. Experimental study not insightful. (i) The experimental study does not describe the experimental setup, most notably hyperparameter optimization. (ii) The proposed methods are sometimes close, sometimes further away in quality compared to related work, but it's not explored why. (iii) In the datasets used in the appendix, the proposed method seems to provide little benefits compared to prior work. (iv) The protocol for evaluating "link prediction" in the experimental study is not convincing: It uses a ranking protocol where only one of the entities in a hyperfact is hidden (e.g., degree) but the others are specified (e.g., university). This problem statement does not appear to be model a practical use case. How would one use such a method to predict a new fact and how good would it be? (v) There are no ablation studies to justify the modelling choices (e.g., just make everything binary, or do not keep the original triple after reification).

W3. Arguments about "semantic difference" unclear. For a fact such as "A graduated at B obtaining degree C", the paper argues that the "graduated at" relation is a primary relation, whereas the degree is auxiliary. This argument is not convincing (one may argue the other way around) to me, and, in fact, the is little justification of the need to capture "semantic difference". Moreover, even if reification is used (i.e., without keeping the original fact) the HKG is still reconstructable.

clarity,_quality,_novelty_and_reproducibility: Clarity. Writing is understandable, but wordy and there are smaller language issues. Theorem 1 is trivial and not needed. Theorem 2 lacks a rigid definitions and is meaningless to me as written.

Novelty. Little (see W1).

Reproducability. Unclear (see W2).

summary_of_the_review: Simple, potentially useful approach, but little novelty and not insightful.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposes TransEQ: a method for hyper-relational knowledge graph embedding. The motivation behind TransEQ is considering both semantic and structural information in a hyper-relational knowledge graph. The proposed method achieves state-of-the-art results on established datasets and strong theoretical properties.

strength_and_weaknesses: This paper studies the important problem of modeling hyper-relational knowledge graphs. 

Despite the simplicity, the proposed approach in the paper achieves state-of-the-art experimental results and strong theoretical properties.

The experimental results are reported on various datasets with different statistics.
clarity,_quality,_novelty_and_reproducibility: The paper is well-written and easy to follow. The code is also available anonymously for reproducibility. 
summary_of_the_review: The method used in the paper to transform an HKG into a KG is almost identical to the reification method discussed in [1]. Fatemi et al [1] showed that transforming the HKG into a KG (conversion approaches) using reification (or S2C) and applying SFs does not work. It is exciting to see that transforming an HKG into a KG and using GNNs+FSs yield better results. I appreciate it if the authors can add detail about this to their paper.

I do not agree with the paper on the importance of semantic information in an HKG. The paper implies that for a hyper-relation educated at, the school is more important than the degree that the corresponding person is educated. This is, however, not given in many datasets and might vary from one dataset to another. Regarding this, can the authors elaborate on how the primary triple in a hyper-relation is identified? Especially for the datasets used in this paper. 

[1] Fatemi, Bahare, Perouz Taslakian, David Vazquez, and David Poole. "Knowledge hypergraphs: Prediction beyond binary relations."
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The authors propose a simple transformation approach to hyper-relational knowledge graph (HKG) modeling with semantic and structural information in this paper. All reviewers agree that the HKG direction is important and a good way to enrich the standard triple-based knowledge graph data structure. However, only one reviewer voted to accept this paper, and three vetoed it. The primary reasons that the reviewers consider this paper not ready for ICLR 2023 is that: 1. The experiments could be strengthened. 2. The choice of scoring function highly affects the generalization. 3. The transformation might make the learning and inference intractable. 4. There are concerns about novelty and insights into the results. Overall, the panel has decided not to recommend this paper for acceptance at ICLR at this time.
justification_for_why_not_higher_score: See above in the meta-review for why this paper was not recommended for acceptance. 
justification_for_why_not_lower_score: N/A.
Decision: Reject