Differentiable learning of numerical rules in knowledge graphs
Rules over a knowledge graph (KG) capture interpretable patterns in data and can be used for KG cleaning and completion. Inspired by the TensorLog differentiable logic framework, which compiles rule inference into a sequence of differentiable operations, recently a method called Neural LP has been proposed for learning the parameters as well as the structure of rules. However, it is limited with respect to the treatment of numerical features like age, weight or scientific measurements. We address this limitation by extending Neural LP to learn rules with numerical values, e.g., “People younger than 18 typically live with their parents”. We demonstrate how dynamic programming and cumulative sum operations can be exploited to ensure efficiency of such extension. Our novel approach allows us to extract more expressive rules with aggregates, which are of higher quality and yield more accurate predictions compared to rules learned by the state-of-the-art methods, as shown by our experiments on synthetic and real-world datasets.
This paper proposes an interesting extension to the Neural LP framework for learning numerical rules in knowledge graphs. The proposed method can handle predicates involving the comparison of the numerical attribute values. The authors demonstrate its effectiveness on both synthetic knowledge graphs and the parts of existing knowledge graphs which consider numerical values. I recommend the paper to be rejected in its current form for the following 3 reasons: (1) Although the idea of making numerical rules differentiable is interesting, the current proposed method can only deal with one form of numerical predicate, which is numerical comparison. The limitation to such a special case makes the paper somewhat incremental. (2) The paper does not do a great job of convincing the reader that the problem it is trying to solve is an important matter, or the proposed method is indeed effective in some applications. Although the proposed method does a good job in synthetic experiments, outperforming existing methods by a large margin, its performance on the numerical variants of Freebase/DBPedia dataset does not show consistent significant improvement. The authors should try to find a real-world domain which can really demonstrate the effectiveness of the method. (3) The experiment section lacks more detailed analysis which can intuitively explain how well the proposed method performs on the benchmarks. A good place to start with is to visualize(print out) the learned numerical rules and see if they make any sense. The experiment section needs significant improvement, especially when there is space left. The authors can consider improving the paper based on the above drawbacks. I encourage the authors to re-submit the paper once it*s improved.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Differentiable learning of numerical rules in knowledge graphs
Rules over a knowledge graph (KG) capture interpretable patterns in data and can be used for KG cleaning and completion. Inspired by the TensorLog differentiable logic framework, which compiles rule inference into a sequence of differentiable operations, recently a method called Neural LP has been proposed for learning the parameters as well as the structure of rules. However, it is limited with respect to the treatment of numerical features like age, weight or scientific measurements. We address this limitation by extending Neural LP to learn rules with numerical values, e.g., “People younger than 18 typically live with their parents”. We demonstrate how dynamic programming and cumulative sum operations can be exploited to ensure efficiency of such extension. Our novel approach allows us to extract more expressive rules with aggregates, which are of higher quality and yield more accurate predictions compared to rules learned by the state-of-the-art methods, as shown by our experiments on synthetic and real-world datasets.
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>



This paper proposes an interesting extension to the Neural LP framework for learning numerical rules in knowledge graphs. The proposed method can handle predicates involving the comparison of the numerical attribute values. The authors demonstrate its effectiveness on both synthetic knowledge graphs and the parts of existing knowledge graphs which consider numerical values. I recommend the paper to be rejected in its current form for the following 3 reasons: (1) Although the idea of making numerical rules differentiable is interesting, the current proposed method can only deal with one form of numerical predicate, which is numerical comparison. The limitation to such a special case makes the paper somewhat incremental. (2) The paper does not do a great job of convincing the reader that the problem it is trying to solve is an important matter, or the proposed method is indeed effective in some applications. Although the proposed method does a good job in synthetic experiments, outperforming existing methods by a large margin, its performance on the numerical variants of Freebase/DBPedia dataset does not show consistent significant improvement. The authors should try to find a real-world domain which can really demonstrate the effectiveness of the method. (3) The experiment section lacks more detailed analysis which can intuitively explain how well the proposed method performs on the benchmarks. A good place to start with is to visualize(print out) the learned numerical rules and see if they make any sense. The experiment section needs significant improvement, especially when there is space left. The authors can consider improving the paper based on the above drawbacks. I encourage the authors to re-submit the paper once it*s improved.
assistant
This paper proposes an interesting extension to the Neural LP framework for learning numerical rules in knowledge graphs. The proposed method can handle predicates involving the comparison of the numerical attribute values. The authors demonstrate its effectiveness on both synthetic knowledge graphs and the parts of existing knowledge graphs which consider numerical values. I recommend the paper to be rejected in its current form for the following 3 reasons: (1) Although the idea of making numerical rules differentiable is interesting, the current proposed method can only deal with one form of numerical predicate, which is numerical comparison. The limitation to such a special case makes the paper somewhat incremental. (2) The paper does not do a great job of convincing the reader that the problem it is trying to solve is an important matter, or the proposed method is indeed effective in some applications. Although the proposed method does a good job in synthetic experiments, outperforming existing methods by a large margin, its performance on the numerical variants of Freebase/DBPedia dataset does not show consistent significant improvement. The authors should try to find a real-world domain which can really demonstrate the effectiveness of the method. (3) The experiment section lacks more detailed analysis which can intuitively explain how well the proposed method performs on the benchmarks. A good place to start with is to visualize(print out) the learned numerical rules and see if they make any sense. The experiment section needs significant improvement, especially when there is space left. The authors can consider improving the paper based on the above drawbacks. I encourage the authors to re-submit the paper once it*s improved.