arXiv Artificial Intelligence

Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

1 hour ago 07.08.2026 07:00 👁 2 views
Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

Quick summary

arXiv:2607.27934v2 Announce Type: replace Abstract: SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known fitting approach to this task: given sets P and N of positive and negative example nodes from an input graph, compute a shape expression C, possibly using shape names defined in a recursive shape catalogue, that validates at every node in P and none in N. We focus on the case where C is written in a core fragment of SHACL corresponding to the Description Logic ELI. For the catalogue, we consider

Key takeaways

  • arXiv:2607.27934v2 Announce Type: replace Abstract: SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications.
  • We investigate the well-known fitting approach to this task: given sets P and N of positive and negative example nodes from an input graph, compute a shape expression C, possibly using shape names defined in a recursive shape catalogue, that validates at every node in P and none in N.
  • We focus on the case where C is written in a core fragment of SHACL corresponding to the Description Logic ELI.

Why it matters

“Shapes from Examples: Foundations of Shape Learning in Recursive SHACL” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗