Summary of Systematic Relational Reasoning with Epistemic Graph Neural Networks, by Irtaza Khalid et al.
Systematic Relational Reasoning With Epistemic Graph Neural Networks
by Irtaza Khalid, Steven Schockaert
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Developing models that can reason is a notoriously challenging problem in relational domains, where Graph Neural Networks (GNNs) seem 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. The proposed Epistemic GNN (EpiGNN) is a novel parameter-efficient and scalable GNN architecture with an epistemic inductive bias for systematic reasoning. Node embeddings in EpiGNNs are treated as epistemic states, and message passing is implemented accordingly. This paper shows that EpiGNNs achieve state-of-the-art results on link prediction tasks that require systematic reasoning, rivaling the performance of specialized approaches for inductive knowledge graph completion. The proposed architecture also learns to reason accurately on two new benchmarks that require the aggregation of information from multiple paths. Code and datasets are available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reasoning is a hard problem in computer science. Researchers have been trying to make computers think more like humans by using Graph Neural Networks (GNNs). However, these networks have limitations when they need to reason about complex relationships between things. A common approach is to use neuro-symbolic methods that learn rules, but this can be slow and assumes too much. This paper proposes a new way to improve GNNs called Epistemic GNNs (EpiGNNs). EpiGNNs are better at reasoning because they treat node embeddings as states of knowledge and pass messages accordingly. The results show that EpiGNNs can solve complex problems like link prediction and knowledge graph completion. In fact, they do better than some specialized approaches. This paper also introduces new benchmarks to test the ability of GNNs to reason about multiple paths. The proposed architecture is available online. |
Keywords
» Artificial intelligence » Gnn » Inference » Knowledge graph » Parameter efficient
