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SHACL形状学习已正式化，并具有严格的复杂度界限

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种用于学习SHACL形状的拟合方法，这对于在知识图谱应用中验证数据图至关重要。该研究侧重于与Description Logic ELI对齐的SHACL核心片段，并考虑了递归形状目录的各种语义。该工作为计算最具体的拟合形状建立了严格的指数时间上限，并解决了其存在性问题，同时还确定了特定场景下的多项式界限。 AI

影响 为知识图谱验证方法提供了正式化，有可能提高AI应用中的数据一致性和可靠性。

排序理由 关于知识图谱验证形式化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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SHACL形状学习已正式化，并具有严格的复杂度界限

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关于知识图谱验证形式化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    Shapes from Examples: Recursive SHACL 中形状学习的基础

    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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