Paper: Efficient Subgraph Rule Induction via Tree Folding in Differentiable Logic Programming (ICLR-2024)

Reviewer comment (Writing):
The paper is very hard to follow, and it is difficult to understand the key difference of the proposed method compared with existing differentiable ILP methods. This is mainly because the paper does not provide many definitions and background knowledge needed to understand it.

Author response:
Thank you for your detailed and well reasoned feedback, we appreciate the time that you spent reviewing our work.

To address some of the questions that you raised:
We have expanded the introduction and motivation of the technique to illustrate why subgraph rule learning is of significance to real world problems. We have also reorganized and clarified the discussion and presentation of the technique, including better high-level descriptions and a new figure to provide improved intuition for the reader. We have improved our explanation of the problem formulation and learning task, as well as the definitions of many of the domain concepts. We have removed the discussion of multi-hop reasoning as it seemed to muddle the presentation of our method. We provide a more clear explanation of how our method formulates ILP as a graph link prediction problem. We have expanded the introduction of our experiments to provide more context on why they were selected and what characteristics of the algorithms they test. We have provided a clarified explanation of the motivation for selecting our baseline method.

Again, we appreciate your time and high-quality feedback.

Reviewer comment (Writing):
There is no explanation of the differentiable ILP task, and the task's input and output are not explained.

Author response:
Thank you for your detailed and well reasoned feedback, we appreciate the time that you spent reviewing our work.

To address some of the questions that you raised:
We have expanded the introduction and motivation of the technique to illustrate why subgraph rule learning is of significance to real world problems. We have also reorganized and clarified the discussion and presentation of the technique, including better high-level descriptions and a new figure to provide improved intuition for the reader. We have improved our explanation of the problem formulation and learning task, as well as the definitions of many of the domain concepts. We have removed the discussion of multi-hop reasoning as it seemed to muddle the presentation of our method. We provide a more clear explanation of how our method formulates ILP as a graph link prediction problem. We have expanded the introduction of our experiments to provide more context on why they were selected and what characteristics of the algorithms they test. We have provided a clarified explanation of the motivation for selecting our baseline method.

Again, we appreciate your time and high-quality feedback.

Reviewer comment (Writing):
The background section starts with explaining multi-hop reasoning as a problem on graphs, but it is unclear how these graphs are related to ILP. The paper must clearly show how differentiable ILP relates to graph problems.

Author response:
Thank you for your detailed and well reasoned feedback, we appreciate the time that you spent reviewing our work.

To address some of the questions that you raised:
We have expanded the introduction and motivation of the technique to illustrate why subgraph rule learning is of significance to real world problems. We have also reorganized and clarified the discussion and presentation of the technique, including better high-level descriptions and a new figure to provide improved intuition for the reader. We have improved our explanation of the problem formulation and learning task, as well as the definitions of many of the domain concepts. We have removed the discussion of multi-hop reasoning as it seemed to muddle the presentation of our method. We provide a more clear explanation of how our method formulates ILP as a graph link prediction problem. We have expanded the introduction of our experiments to provide more context on why they were selected and what characteristics of the algorithms they test. We have provided a clarified explanation of the motivation for selecting our baseline method.

Again, we appreciate your time and high-quality feedback.

Reviewer comment (Presentation):
In its current form, the paper is almost impossible to understand for many readers, despite potentially containing important ideas. A major revision is needed to improve its presentation.

Author response:
Thank you for your detailed and well reasoned feedback, we appreciate the time that you spent reviewing our work.

To address some of the questions that you raised:
We have expanded the introduction and motivation of the technique to illustrate why subgraph rule learning is of significance to real world problems. We have also reorganized and clarified the discussion and presentation of the technique, including better high-level descriptions and a new figure to provide improved intuition for the reader. We have improved our explanation of the problem formulation and learning task, as well as the definitions of many of the domain concepts. We have removed the discussion of multi-hop reasoning as it seemed to muddle the presentation of our method. We provide a more clear explanation of how our method formulates ILP as a graph link prediction problem. We have expanded the introduction of our experiments to provide more context on why they were selected and what characteristics of the algorithms they test. We have provided a clarified explanation of the motivation for selecting our baseline method.

Again, we appreciate your time and high-quality feedback.

Reviewer comment (Experiments):
The experimental evaluation section is weak because there are no explanations of the details of experimental settings.

Author response:
Thank you for your detailed and well reasoned feedback, we appreciate the time that you spent reviewing our work.

To address some of the questions that you raised:
We have expanded the introduction and motivation of the technique to illustrate why subgraph rule learning is of significance to real world problems. We have also reorganized and clarified the discussion and presentation of the technique, including better high-level descriptions and a new figure to provide improved intuition for the reader. We have improved our explanation of the problem formulation and learning task, as well as the definitions of many of the domain concepts. We have removed the discussion of multi-hop reasoning as it seemed to muddle the presentation of our method. We provide a more clear explanation of how our method formulates ILP as a graph link prediction problem. We have expanded the introduction of our experiments to provide more context on why they were selected and what characteristics of the algorithms they test. We have provided a clarified explanation of the motivation for selecting our baseline method.

Again, we appreciate your time and high-quality feedback.

Reviewer comment (Novelty):
If the novel contributions were better highlighted it would be useful to evaluate significance of the proposed method.

Author response:
We appreciate your time and the valuable feedback that you have provided on our work.

To address some of your questions:
We have updated the paper to include an expanded discussion of the significance of non-chain-like relationships and we provide a motivating example to illustrate how they can appear. We have also reorganized and clarified our discussion of the mechanisms that we employ to enable subgraph-like rule learning, as well as how those differ from chain-like rule learners.

Again, thank you for contributing your time to our paper.

Reviewer comment (Novelty):
As the authors mention, the message passing is an improvement on the same proposal for Tor TensorLog. Can you please further clarify your contribution in this?

Author response:
Thank you for the feedback on our paper, we appreciate your time.

To address some of the questions you had: We have expanded the introduction and motivation to better illustrate how real world problems may exhibit naturally non-chain-like behavior. We have also improved the narrative of our discussion to clarify that TensorLog is a database system that describes differentiable rule inference, and not another ILP technique. We have updated our explanations of the satisfiability derivation algorithm to improve the clarity of the SAT problem and our descriptions of it.

Thank you.

Reviewer comment (Writing):
What is the difference between T and SAT/ Thanks!

Author response:
Thank you for the feedback on our paper, we appreciate your time.

To address some of the questions you had: We have expanded the introduction and motivation to better illustrate how real world problems may exhibit naturally non-chain-like behavior. We have also improved the narrative of our discussion to clarify that TensorLog is a database system that describes differentiable rule inference, and not another ILP technique. We have updated our explanations of the satisfiability derivation algorithm to improve the clarity of the SAT problem and our descriptions of it.

Thank you.