Kati8/ICLR_2023_2025 huggingface dataset:
authors: Sun Kai
authors: Huajie Jiang
authors: Yongli Hu
authors: Baocai Yin
reviews: 
Comments: This paper addresses the issue of noise in message-passing Graph Neural Networks (GNNs) used in inductive knowledge graph completion, where irrelevant entities and edges introduce noise that accumulates exponentially, hindering model performance. A dual denoising logical reasoning (DDLR) framework is proposed that integrates path-based and edge-based sampling mechanisms. This framework employs a path-scoring mechanism to evaluate and remove irrelevant paths and leverages rules within the knowledge graph to exclude irrelevant edges, aiming for comprehensive denoising. Experiments conducted on three benchmark datasets show that the DDLR framework achieves state-of-the-art performance, demonstrating its effectiveness in enhancing logical reasoning in GNN-based knowledge graph reasoning.
Strengths: 1.	The proposed method can effectively enhance knowledge graph completion by integrating dual denoising techniques operating at both the path and edge levels.
2.	The authors conducted extensive experiments to demonstrate the effectiveness of the method, which presented the performance for each component from various perspectives.
Weaknesses: 1.	The paper is not organized clearly, which is not friendly for understanding. Specifically, there are lack the detailed discussion about related work [1].
2. This paper should be sent to the ICLR ethical board review committee on the subject of scientific misconduct and there are some ethics problems in this paper according to the A*Net paper [1].
[1] A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Recommendation: 1: strong reject
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 1 poor
Clarity: 2 fair
reviews: 
Comments: This paper proposes a message-passing mechanism for inductive knowledge graph completion.

Given the fact that this method is highly similar to the existing A*Net, I don't think the contribution was properly justified in the current version.
Strengths: The performance is reasonably good, given the success of A*Net and GraPE. Additionally to its predecessors, section 4.2 is somewhat true.
Weaknesses: The only part of the model that is a significant difference between this work and A*Net is Section 4.2.

I don't see any excuses for not comparing this work with A*Net, especially since this paper includes a more recent arxived baseline GraPE.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 2 fair
reviews: 
Comments: The paper proposes a new framework called Dual Denoising Logical Reasoning (DDLR) for inductive knowledge graph completion. Current GNN-based methods suffer from noise accumulation when propagating messages freely through graphs. This impacts performance. DDLR employs dual denoising through path-based and edge-based sampling to remove irrelevant information. For path sampling, a novel path scoring mechanism is proposed to evaluate path importance for selecting relevant paths. For edge sampling, single rules extracted from the graph are used to sample relevant edges and filter out irrelevant ones. Experiments on 3 datasets show DDLR outperforms state-of-the-art methods for inductive knowledge graph completion.
Strengths: * The dual denoising approach combining path and edge sampling is innovative. It allows comprehensive filtering of noise from both entities and relations.

* Extensive experiments validate the effectiveness of DDLR, demonstrating superior accuracy over state-of-the-art baselines.
Weaknesses: A few sections of the paper are nearly identical or highly similar to the A\*Net paper [1]. However, the A\*Net paper is not cited anywhere. This raises ethical concerns, and puts the novel contributions of this work into question. The authors need to clearly distinguish their contributions compared to A\*Net and other prior works through proper citation and explanation. As presented, the incremental novelty is unclear since the core ideas appear derived from A\*Net without attribution. This is a major weakness.

[1] A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Recommendation: 3: reject, not good enough
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good