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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
Shapes from Examples: Foundations of Shape Learning in Recursive SHACL
TL;DR
Recursive SHACL shapes enable automatic shape learning for validating knowledge graphs. This study establishes efficient methods for computing shape expressions that fit positive examples while excluding negative ones.
✦ Why It Matters
Implement automatic shape learning in your knowledge graph applications to improve data validation accuracy.
Key Takeaways
- Utilize Recursive SHACL for effective data graph validation.
- Apply the fitting approach to derive shape expressions from examples.
- Leverage established bounds for efficient shape computation.
- Explore special cases for polynomial time solutions.
How It Works
The fitting approach involves analyzing sets of positive and negative example nodes to compute a shape expression that validates all nodes in the positive set while excluding those in the negative set. By leveraging a recursive shape catalogue, the method can utilize predefined shape names to construct the desired shape expression efficiently.