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SHACL shape learning formalized with tight complexity bounds

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a fitting approach for learning SHACL shapes, which are crucial for validating data graphs in knowledge graph applications. The study focuses on a core fragment of SHACL that aligns with the Description Logic ELI, considering various semantics for recursive shape catalogues. The work establishes tight exponential-time upper bounds for computing the most specific fitting shape and addresses its existence, while also identifying polynomial bounds for specific scenarios. AI

IMPACT Formalizes methods for knowledge graph validation, potentially improving data consistency and reliability in AI applications.

RANK_REASON Academic paper on formal methods for knowledge graph validation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SHACL shape learning formalized with tight complexity bounds

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Academic paper on formal methods for knowledge graph validation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bente Gortworst, Cem Okulmus, Magdalena Ortiz, Anni-Yasmin Turhan ·

    Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

    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 exam…

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