Summary of Relational Graph Convolutional Networks Do Not Learn Sound Rules, by Matthew Morris et al.
Relational Graph Convolutional Networks Do Not Learn Sound Rules
by Matthew Morris, David J. Tena Cucala, Bernardo Cuenca Grau, Ian Horrocks
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
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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 The proposed work focuses on developing methods for explaining the predictions made by Graph Neural Networks (GNNs) in Knowledge Graphs (KGs). Specifically, it addresses the lack of explainability in R-GCN, a popular GNN architecture for KGs. The authors provide two methods to extract rules that explain the predictions and are sound, ensuring that each fact derived is also predicted by the GNN. Additionally, they introduce a method to verify that certain Datalog rules are not sound for R-GCN. Experimental results on KG completion benchmarks show that no Datalog rule is sound for these models, despite high accuracy. This raises concerns about generalization and explainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates ways to make predictions from Graph Neural Networks (GNNs) more understandable in Knowledge Graphs (KGs). It specifically looks at the R-GCN model, which is commonly used for this task. The authors create two methods to find simple rules that explain why the GNN made a certain prediction. They also develop a way to check if these rules are actually good explanations. When they test their ideas on real datasets, they find that no simple rule can perfectly explain the predictions of the R-GCN model. This might be a problem because it means the model might not work well in new situations. |
Keywords
» Artificial intelligence » Gcn » Generalization » Gnn
