Systematic Relational Reasoning With Epistemic Graph Neural Networks
Irtaza Khalid, Steven Schockaert
TL;DR
This paper introduces EpiGNN, a novel epistemic graph neural network architecture that enhances systematic relational reasoning, outperforming existing models on complex link prediction and knowledge graph tasks, and introduces new challenging benchmarks.
Contribution
EpiGNN incorporates an epistemic inductive bias into GNNs, enabling scalable and systematic reasoning beyond existing neuro-symbolic methods.
Findings
EpiGNN achieves state-of-the-art results on link prediction tasks requiring systematic reasoning.
EpiGNN rivals specialized approaches in inductive knowledge graph completion.
EpiGNN successfully learns to reason on new benchmarks involving multi-path information aggregation.
Abstract
Developing models that can learn to reason is a notoriously challenging problem. We focus on reasoning in relational domains, where the use of Graph Neural Networks (GNNs) seems like a natural choice. However, previous work has shown that regular GNNs lack the ability to systematically generalize from training examples on test graphs requiring longer inference chains, which fundamentally limits their reasoning abilities. A common solution relies on neuro-symbolic methods that systematically reason by learning rules, but their scalability is often limited and they tend to make unrealistically strong assumptions, e.g.\ that the answer can always be inferred from a single relational path. We propose the Epistemic GNN (EpiGNN), a novel parameter-efficient and scalable GNN architecture with an epistemic inductive bias for systematic reasoning. Node embeddings in EpiGNNs are treated as…
Peer Reviews
Decision·ICLR 2025 Poster
1. The method introduced here demonstrates significantly higher efficiency compared to other baselines, as clearly illustrated in Figure 5. 2. The proposed method is compared against various baselines, including both rule-based and graph-based approaches, and consistently shows superior performance. 3. New synthetic datasets are provided in this work.
1. In my opinion, representing each node/entity as a distribution over relations is unconventional. Despite its parameter efficiency and superior performance, this embedding/representation method has notable drawbacks: (1) The learned embeddings are non-transferable across datasets, as different datasets may have varying numbers of relations. (2) It completely ignores the semantic information of entities, making the semantic interpretation of these representations difficult. 2. The notations in
- This paper focuses on longer logic inference with requirement of multi-path disjunctive reasoning, which are critical problems in knowledge graph. - This paper's solution is based on scalable framework, making it useful in giant knowledge graphs in real world. - This paper has prove of expressivity in the sense of logical reasoning, promising its power to handle focusing problems.
- Some design details have potentially alternatives, but is lack of reasoning, leaving concerns that if the implementation is the best design under proposed framework. - The empirical evidence is not strong enough to showcase their claims: - The studying problem is claimed to be unsolvable for existing methods, while baselines can achieve the best performance. - The method is claimed to be more scalable than related baselines, however, there is runtime or memory cost comparison in main con
- This paper introduce two new datasets for benchmarking systematic generalization. The two new datasets consider multi-path disjunctive rules, which is challenging for most existing methods. - EpiGNNs achieve state-of-the-art performance on the two new datasets. EpiGNNs also achieve competitive performance against existing methods on systematic generalization datasets, while being more efficient. - EpiGNNs have theoretical connection with the algebraic closure algorithm, though this point is no
- EpiGNNs are variants of NBFNet[1] with some engineering modifications: 1) hidden representations are replaced by probability distributions; 2) DistMult message function is replaced by Tucker decomposition[2] (Equation 4); 3) pooling function has an additional L1 normalization; 4) joint training of multiple models and forward-backward models. While I am not saying that EpiGNNs are incremental, the authors need to show how these modifications contribute to systematic generalization and provide e
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